Atmospheric aerosols affect the Earth’s climate in many ways, including acting as the seeds on which cloud droplets form. Since a large fraction of these particles is anthropogenic, the clouds’ microphysical and radiative characteristics are influenced by human activity on a global scale leading to important climatic effects. The respective change in the energy budget at the top of the atmosphere is defined as the effective radiative forcing due to aerosol-cloud interaction (ERFaci). It is estimated that the ERFaci offsets presently nearly 1/4 of the greenhouse-induced warming, but the uncertainty is within a factor of two. A common method to calculate the ERFaci is by the multiplication of the susceptibility of the cloud radiative effect to changes in aerosols by the anthropogenic change of the aerosol concentration. This has to be done by integrating it over all cloud regimes. Here we review the various methods of the ERFaci estimation. Global measurements require satellites’ global coverage. The challenge of quantifying aerosol amounts in cloudy atmospheres are met with the rapid development of novel methodologies reviewed here. The aerosol characteristics can be retrieved from space based on their optical properties, including polarization. The concentrations of the aerosols that serve as cloud drop condensation nuclei can be also estimated from their impact on the satellite-retrieved cloud drop number concentrations. These observations are critical for reducing the uncertainty in the ERFaci calculated from global climate models (GCMs), but further development is required to allow GCMs to properly simulate and benefit these novel observables.
Movement restrictions were imposed in 2020 to mitigate the spread of Covid-19. These lock-down episodes provide a unique opportunity to study the sensitivity of urban photochemistry to temporary emission reductions and test air quality models. This study uses Tropospheric Monitoring Instrument (TROPOMI) nitrogen dioxide/carbon monoxide (NO2/CO) ratios in urban plumes in combination with an exponential fitting procedure to infer changes in the NOx lifetime (τNOx) during Covid-19 lock-downs in the cities of Denver, Chicago, New York, Riyadh, Wuhan and Sao Paulo compared with the year before.
The strict lockdown policy in Wuhan led to a 65-80% reduction in NO2, compared to 30-50% in the other cities that were studied. In New York and Wuhan, CO concentration was reduced by 10-15%, whereas over Riyadh, Denver, Chicago, and Sao Paulo the CO background concentration increased by 2-5 ppb. τNOx has been derived for calm (0.0 < U (m/s) < 3.5) and windy (5.0 < U (m/s) < 8.5) days to study the influence of wind speed. We find reductions in τNOx during Covid-19 lockdowns in all six megacities during calm days. The largest change in τNOx during calm days is found for Sao-Paulo (31.8 ± 9.0%), whereas the smallest reduction is observed over Riyadh (22 ± 6.6%). During windy days, reductions in τNOx are observed during Covid-19 lockdowns in New York and Chicago. However, over Riyadh τNOx is almost similar for windy days during the Covid-19 lockdown and the year before. Ground-based measurements and the Chemistry Land-surface Atmosphere Soil Slab (CLASS) model have been used to validate the TROPOMI-derived results over Denver. CLASS simulates an enhancement of ozone (O3) by 4 ppb along with reductions in NO (38.7%), NO2 (25.7%) and CO (17.2%) during the Covid-19 lockdown in agreement with the ground-based measurements. In CLASS, decreased NOx emissions reduce the removal of OH in the NO2 + OH reaction, leading to higher OH concentrations and decreased τNOx . The reduction in τNOx inferred from TROPOMI (28 ± 9.0%) is in agreement with CLASS. These results indicate that TROPOMI derived NO2/CO ratios provide useful information about urban photochemistry and that changes in photochemical lifetimes can successfully be detected.A reduction in anthropogenic methane emissions is vital to limit near-term global warming. A small number of so-called super-emitters is responsible for a disproportionally large fraction of total methane emissions. Since late 2017, the TROPOspheric Monitoring Instrument (TROPOMI) has been in orbit, providing daily global coverage of methane mixing ratios at a resolution of up to 7×5.5 km2, enabling the detection of these super-emitters. However, TROPOMI produces millions of observations each day, which together with the complexity of the methane data, makes manual inspection infeasible. We have therefore designed a two-step machine learning approach using a convolutional neural network to detect plume-like structures in the methane data and subsequently apply a support vector classifier to distinguish the emission plumes from retrieval artifacts. The models are trained on pre-2021 data and subsequently applied to all 2021 observations. We detect 2974 plumes in 2021, with a mean estimated source rate of 44 t h−1 and 5-95th percentile range of 8-122 t h−1. These emissions originate from 94 persistent emission clusters and hundreds of transient sources. Based on bottom-up emission inventories, we find that most detected plumes are related to urban areas and/or landfills (35 %), followed by plumes from gas infrastructure (24 %), oil infrastructure (21 %), and coal mines (20 %). For 12 (clusters of) TROPOMI detections, we tip and cue the targeted observations and analysis of high-resolution satellite instruments to identify the exact sources responsible for these plumes. Using high-resolution observations from GHGSat, PRISMA, and Sentinel-2, we detect and analyze both persistent and transient facility-level emissions underlying the TROPOMI detections. We find emissions from landfills and fossil fuel exploitation facilities, and for the latter, we find up to 10 facilities contributing to one TROPOMI detection. Our automated TROPOMI-based monitoring system in combination with high-resolution satellite data allows for the detection, precise identification, and monitoring of these methane super-emitters, which is essential for mitigating their emissions.
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