Surface-to-space atmospheric waves from Hunga Tonga-Hunga Ha'apai eruption.

The January 2022 Hunga Tonga-Hunga Ha'apai eruption was one of the most explosive volcanic events of the modern era1,2, producing a vertical plume that peaked more than 50 km above the Earth3. The initial explosion and subsequent plume triggered atmospheric waves that propagated around the world multiple times4. A global-scale wave response of this magnitude from a single source has not previously been observed. Here we show the details of this response, using a comprehensive set of satellite and ground-based observations to quantify it from surface to ionosphere. A broad spectrum of waves was triggered by the initial explosion, including Lamb waves5,6 propagating at phase speeds of 318.2 ± 6 m s-1 at surface level and between 308 ± 5 to 319 ± 4 m s-1 in the stratosphere, and gravity waves7 propagating at 238 ± 3 to 269 ± 3 m s-1 in the stratosphere. Gravity waves at sub-ionospheric heights have not previously been observed propagating at this speed or over the whole Earth from a single source8,9. Latent heat release from the plume remained the most significant individual gravity wave source worldwide for more than 12 h, producing circular wavefronts visible across the Pacific basin in satellite observations. A single source dominating such a large region is also unique in the observational record. The Hunga Tonga eruption represents a key natural experiment in how the atmosphere responds to a sudden point-source-driven state change, which will be of use for improving weather and climate models.

Industrial and agricultural ammonia point sources exposed.

Through its important role in the formation of particulate matter, atmospheric ammonia affects air quality and has implications for human health and life expectancy1,2. Excess ammonia in the environment also contributes to the acidification and eutrophication of ecosystems3-5 and to climate change6. Anthropogenic emissions dominate natural ones and mostly originate from agricultural, domestic and industrial activities7. However, the total ammonia budget and the attribution of emissions to specific sources remain highly uncertain across different spatial scales7-9. Here we identify, categorize and quantify the world's ammonia emission hotspots using a high-resolution map of atmospheric ammonia obtained from almost a decade of daily IASI satellite observations. We report 248 hotspots with diameters smaller than 50 kilometres, which we associate with either a single point source or a cluster of agricultural and industrial point sources-with the exception of one hotspot, which can be traced back to a natural source. The state-of-the-art EDGAR emission inventory10 mostly agrees with satellite-derived emission fluxes within a factor of three for larger regions. However, it does not adequately represent the majority of point sources that we identified and underestimates the emissions of two-thirds of them by at least one order of magnitude. Industrial emitters in particular are often found to be displaced or missing. Our results suggest that it is necessary to completely revisit the emission inventories of anthropogenic ammonia sources and to account for the rapid evolution of such sources over time. This will lead to better health and environmental impact assessments of atmospheric ammonia and the implementation of suitable nitrogen management strategies.

Monthly Patterns of Ammonia Over the Contiguous United States at 2-km Resolution.

Monthly, high-resolution (∼2 km) ammonia (NH3) column maps from the Infrared Atmospheric Sounding Interferometer (IASI) were developed across the contiguous United States and adjacent areas. Ammonia hotspots (95th percentile of the column distribution) were highly localized with a characteristic length scale of 12 km and median area of 152 km2. Five seasonality clusters were identified with k-means++ clustering. The Midwest and eastern United States had a broad, spring maximum of NH3 (67% of hotspots in this cluster). The western United States, in contrast, showed a narrower midsummer peak (32% of hotspots). IASI spatiotemporal clustering was consistent with those from the Ammonia Monitoring Network. CMAQ and GFDL-AM3 modeled NH3 columns have some success replicating the seasonal patterns but did not capture the regional differences. The high spatial-resolution monthly NH3 maps serve as a constraint for model simulations and as a guide for the placement of future, ground-based network sites.

Remote sensing and model analysis of biomass burning smoke transported across the Atlantic during the 2020 Western US wildfire season.

Biomass burning is the main source of air pollution in several regions worldwide nowadays. This predominance is expected to increase in the upcoming years as a result of the rising number of devastating wildfires due to climate change. Harmful pollutants contained in the smoke emitted by fires can alter downwind air quality both locally and remotely as a consequence of the recurrent transport of biomass burning plumes across thousands of kilometers. Here, we demonstrate how observations of carbon monoxide and aerosol optical depth retrieved from polar orbiting and geostationary meteorological satellites can be used to study the long-range transport and evolution of smoke plumes. This is illustrated through the megafire events that occurred during summer 2020 in the Western United States and the transport of the emitted smoke across the Atlantic Ocean to Europe. Analyses from the Copernicus Atmosphere Monitoring Service, which combine satellite observations with an atmospheric model, are used for comparison across the region of study and along simulated air parcel trajectories. Lidar observation from spaceborne and ground-based instruments are used to verify consistency of passive observations. Results show the potential of joint satellite-model analysis to understand the emission, transport, and processing of smoke across the world.

Ethylene industrial emitters seen from space.

Volatile organic compounds are emitted abundantly from a variety of natural and anthropogenic sources. However, in excess, they can severely degrade air quality. Their fluxes are currently poorly represented in inventories due to a lack of constraints from global measurements. Here, we track from space over 300 worldwide hotspots of ethylene, the most abundant industrially produced organic compound. We identify specific emitters associated with petrochemical clusters, steel plants, coal-related industries, and megacities. Satellite-derived fluxes reveal that the ethylene emissions of the industrial sources are underestimated or missing in the state-of-the-art Emission Database for Global Atmospheric Research (EDGAR) inventory. This work exposes global emission point-sources of a short-lived carbonated gas, complementing the ongoing large-scale efforts on the monitoring of inorganic pollutants.

On the weekly cycle of atmospheric ammonia over European agricultural hotspots.

The presence of a weekly cycle in the abundance of an atmospheric constituent is a typical fingerprint for the anthropogenic nature of its emission sources. However, while ammonia is mainly emitted as a consequence of human activities, a weekly cycle has never been detected in its abundances at large scale. We expose here for the first time the presence of a weekend effect in the NH3 total columns measured by the IASI satellite sounder over the main agricultural source regions in Europe: northwestern Europe (Belgium-the Netherlands-northwest Germany), the Po Valley, Brittany, and, to a lesser extent, the Ebro Valley. A decrease of 15% relative to the weekly mean is seen on Sunday-Monday observations in northwestern Europe, as a result of reduced NH3 emissions over the weekend. This is confirmed by in situ NH3 concentration data from the National Air Quality Monitoring Network in the Netherlands, where an average reduction of 10% is found around midnight on Sunday. The identified weekend effect presents a strong seasonal variability, with two peaks, one in spring and one in summer, coinciding with the two main (manure) fertilization periods. In spring, a reduction on Sunday-Monday up to 53 and 26% is found in the NH3 satellite columns and in situ concentrations, respectively, as fertilization largely drives atmospheric NH3 abundances at this time of the year.

IASI-Derived Sea Surface Temperature Data Set for Climate Studies.

Sea surface temperature (SST) is an essential climate variable, that is directly used in climate monitoring. Although satellite measurements can offer continuous global coverage, obtaining a long-term homogeneous satellite-derived SST data set suitable for climate studies based on a single instrument is still a challenge. In this work, we assess a homogeneous SST data set derived from reprocessed Infrared Atmospheric Sounding Interferometer (IASI) level-1 (L1C) radiance data. The SST is computed using Planck's Law and simple atmospheric corrections. We assess the data set using the ERA5 reanalysis and the EUMETSAT-released IASI level-2 SST product. Over the entire period, the reprocessed IASI SST shows a mean global difference with ERA5 close to zero, a mean absolute bias under 0.5°C, with a SD of difference around 0.3°C and a correlation coefficient over 0.99. In addition, the reprocessed data set shows a stable bias and SD, which is an advantage for climate studies. The interannual variability and trends were compared with other SST data sets: ERA5, Hadley Centre's SST (HadISST), and NOAA's Optimal Interpolation SST Analysis (OISSTv2). We found that the reprocessed SST data set is able to capture the patterns of interannual variability well, showing the same areas of high interannual variability (>1.5°C), including over the tropical Pacific in January corresponding to the El Niño Southern Oscillation. Although the period studied is relatively short, we demonstrate that the IASI data set reproduces the same trend patterns found in the other data sets (i.e., cooling trend in the North Atlantic, warming trend over the Mediterranean).

Retrieving radius, concentration, optical depth, and mass of different types of aerosols from high-resolution infrared nadir spectra.

We present a sophisticated radiative transfer code for modeling outgoing IR radiation from planetary atmospheres and, conversely, for retrieving atmospheric properties from high-resolution nadir-observed spectra. The forward model is built around a doubling-adding routine and calculates, in a spherical refractive geometry, the outgoing radiation emitted by the Earth and the atmosphere containing one layer of aerosol. The inverse model uses an optimal estimation approach and can simultaneously retrieve atmospheric trace gases, aerosol effective radius, and concentration. It is different from existing codes, as most forward codes dealing with multiple scattering assume a plane-parallel atmosphere, and as for the retrieval, it does not rely on precalculated spectra, the use of microwindows, or two-step retrievals. The simultaneous retrieval on a broad spectral range exploits the full potential of current state-of-the-art hyperspectral IR sounders, such as AIRS and IASI, and should be particularly useful in studying major pollution events. We present five example retrievals of IASI spectra observed in the range from 800 to 1200 cm(-1) above dust, volcanic ash, sulfuric acid, ice particles, and biomass burning aerosols.

Monitoring emissions from the 2015 Indonesian fires using CO satellite data.

Southeast Asia, in particular Indonesia, has periodically struggled with intense fire events. These events convert substantial amounts of carbon stored as peat to atmospheric carbon dioxide (CO2) and significantly affect atmospheric composition on a regional to global scale. During the recent 2015 El Niño event, peat fires led to strong enhancements of carbon monoxide (CO), an air pollutant and well-known tracer for biomass burning. These enhancements were clearly observed from space by the Infrared Atmospheric Sounding Interferometer (IASI) and the Measurements of Pollution in the Troposphere (MOPITT) instruments. We use these satellite observations to estimate CO fire emissions within an inverse modelling framework. We find that the derived CO emissions for each sub-region of Indonesia and Papua are substantially different from emission inventories, highlighting uncertainties in bottom-up estimates. CO fire emissions based on either MOPITT or IASI have a similar spatial pattern and evolution in time, and a 10% uncertainty based on a set of sensitivity tests we performed. Thus, CO satellite data have a high potential to complement existing operational fire emission estimates based on satellite observations of fire counts, fire radiative power and burned area, in better constraining fire occurrence and the associated conversion of peat carbon to atmospheric CO2 A total carbon release to the atmosphere of 0.35-0.60 Pg C can be estimated based on our results.This article is part of a discussion meeting issue 'The impact of the 2015/2016 El Niño on the terrestrial tropical carbon cycle: patterns, mechanisms and implications'.

Towards a climate-dependent paradigm of ammonia emission and deposition.

Existing descriptions of bi-directional ammonia (NH3) land-atmosphere exchange incorporate temperature and moisture controls, and are beginning to be used in regional chemical transport models. However, such models have typically applied simpler emission factors to upscale the main NH3 emission terms. While this approach has successfully simulated the main spatial patterns on local to global scales, it fails to address the environment- and climate-dependence of emissions. To handle these issues, we outline the basis for a new modelling paradigm where both NH3 emissions and deposition are calculated online according to diurnal, seasonal and spatial differences in meteorology. We show how measurements reveal a strong, but complex pattern of climatic dependence, which is increasingly being characterized using ground-based NH3 monitoring and satellite observations, while advances in process-based modelling are illustrated for agricultural and natural sources, including a global application for seabird colonies. A future architecture for NH3 emission-deposition modelling is proposed that integrates the spatio-temporal interactions, and provides the necessary foundation to assess the consequences of climate change. Based on available measurements, a first empirical estimate suggests that 5°C warming would increase emissions by 42 per cent (28-67%). Together with increased anthropogenic activity, global NH3 emissions may increase from 65 (45-85) Tg N in 2008 to reach 132 (89-179) Tg by 2100.

Retrieval of CO from nadir remote-sensing measurements in the infrared by use of four different inversion algorithms.

Four inversion schemes based on various retrieval approaches (digital gas correlation, nonlinear least squares, global fit adjustment, and neural networks) developed to retrieve CO from nadir radiances measured by such downward-looking satelliteborne instruments as the Measurement of Pollution in the Troposphere (MOPITT), the Tropospheric Emission Spectrometer (TES), and the Infrared Atmospheric Sounding Interferometer (IASI) instruments were compared both for simulated cases and for atmospheric spectra recorded by the Interferometric Monitor for Greenhouse Gases (IMG). The sensitivity of the retrieved CO total column amount to properties that may affect the inversion accuracy (noise, ancillary temperature profile, and water-vapor content) was investigated. The CO column amounts for the simulated radiance spectra agreed within 4%, whereas larger discrepancies were obtained when atmospheric spectra recorded by the IMG instrument were analyzed. The assumed vertical temperature profile is shown to be a critical parameter for accurate CO retrieval. The instrument's line shape was also identified as a possible cause of disagreement among the result provided by the groups of scientist who are participating in this study.