Detecting anthropogenic cloud perturbations with deep learning

One of the most pressing questions in climate science is that of the effect of anthropogenic aerosol on the Earth's energy balance. Aerosols provide the `seeds' on which cloud droplets form, and changes in the amount of aerosol available to a cloud can change its brightness and other physical properties such as optical thickness and spatial extent. Clouds play a critical role in moderating global temperatures and small perturbations can lead to significant amounts of cooling or warming. Uncertainty in this effect is so large it is not currently known if it is negligible, or provides a large enough cooling to largely negate present-day warming by CO2. This work uses deep convolutional neural networks to look for two particular perturbations in clouds due to anthropogenic aerosol and assess their properties and prevalence, providing valuable insights into their climatic effects.Comment: Awarded Best Paper and Spotlight Oral at Climate Change: How Can AI Help? (Workshop) at International Conference on Machine Learning (ICML), Long Beach, California, 201

Pollution tracker: finding industrial sources of aerosol emission in satellite imagery

The effects of anthropogenic aerosol, solid or liquid particles suspended in the air, are the biggest contributor to uncertainty in current climate perturbations. Heavy industry sites, such as coal power plants and steel manufacturers, emit large amounts of aerosol in a small area. This makes them ideal places to study aerosol interactions with radiation and clouds. However, existing data sets of heavy industry locations are either not public, or suffer from reporting gaps. Here, we develop a deep learning algorithm to detect unreported industry sites in high-resolution satellite data. For the pipeline to be viable at global scale, we employ a two-step approach. The first step uses 10 m resolution data, which is scanned for potential industry sites, before using 1.2 m resolution images to confirm or reject detections. On held out test data, the models perform well, with the lower resolution one reaching up to 94% accuracy. Deployed to a large test region, the first stage model yields many false positive detections. The second stage, higher resolution model shows promising results at filtering these out, while keeping the true positives. In the deployment area, we find five new heavy industry sites which were not in the training data. This demonstrates that the approach can be used to complement data sets of heavy industry sites

CloudTracks: A Dataset for Localizing Ship Tracks in Satellite Images of Clouds

Clouds play a significant role in global temperature regulation through their effect on planetary albedo. Anthropogenic emissions of aerosols can alter the albedo of clouds, but the extent of this effect, and its consequent impact on temperature change, remains uncertain. Human-induced clouds caused by ship aerosol emissions, commonly referred to as ship tracks, provide visible manifestations of this effect distinct from adjacent cloud regions and therefore serve as a useful sandbox to study human-induced clouds. However, the lack of large-scale ship track data makes it difficult to deduce their general effects on cloud formation. Towards developing automated approaches to localize ship tracks at scale, we present CloudTracks, a dataset containing 3,560 satellite images labeled with more than 12,000 ship track instance annotations. We train semantic segmentation and instance segmentation model baselines on our dataset and find that our best model substantially outperforms previous state-of-the-art for ship track localization (61.29 vs. 48.65 IoU). We also find that the best instance segmentation model is able to identify the number of ship tracks in each image more accurately than the previous state-of-the-art (1.64 vs. 4.99 MAE). However, we identify cases where the best model struggles to accurately localize and count ship tracks, so we believe CloudTracks will stimulate novel machine learning approaches to better detect elongated and overlapping features in satellite images. We release our dataset openly at {zenodo.org/records/10042922}.Comment: 11 pages, 5 figures, submitted to Journal of Machine Learning Researc

Tööstusheitmetest tugevalt saastunud pilved aitavad mõista inimtegevuse kliimamõju

Väitekirja elektrooniline versioon ei sisalda publikatsiooneInimtekkelised õhusaasteosakesed jahutavad Maa kliimat ja kompenseerivad osa kasvuhoonegaaside soojendavast kliimamõjust. Õhusaasteosakeste jahutava kliimamõju täpne ulatus on ebaselge, kusjuures määramatus võrreldes kasvuhoonegaaside soojendava efektiga palju suurem. Kõige ebaselgem on seejuures õhusaasteosakeste mõju pilvedele. Pilved ja sademed tekivad Maa atmosfääris tänu sellele, et õhku on pihustunud tahkeid või vedelaid väikeseid osakesi, mille ümber veeaur kondenseerub. Inimtekkelise õhusaaste tõttu on atmosfääris selliseid väikeseid osakesi rohkem, ning see põhjustab muutusi pilvede omadustes. Antud töös võrdleme isoleeritud saasteallikate heitmetest tugevalt saastunud pilvede omadusi kõrvalasuvate saastumata pilvedega. Täpsustame inimtekkeliste õhusaasteosakeste kliimamõju ulatust, analüüsides kui sagedasti ja millistes ilmaoludes on inimtekkelistel õhusaasteosakestel tugev mõju pilvede omadustele. Näitame, et vastupidi kliimamudelites kasutatud eeldusele, saasteosakeste mõjul pilvede keskmine paksus ei kasva. See tulemus viitab, et õhusaasteosakeste jahutav mõju ei ole nii tugev nagu seni on arvatud. Doktoritöö tulemused aitavad luua usaldusväärsemaid tuleviku kliima projektsioone.It is unknown to what extent the cooling effect of the anthropogenic air pollution particles called aerosols offsets the warming effect of greenhouse gases. It is especially uncertain how strong is the cooling effect exerted by aerosol-induced changes in cloud properties. Clouds and precipitation can form in the Earth’s atmosphere thanks to the fine solid and liquid particles suspended in the atmosphere. Due to anthropogenic air pollution, there are more aerosol particles in the atmosphere leading to changes in cloud properties. Here, we compare the properties of clouds polluted by emissions from strong isolated aerosol sources to the nearby unpolluted clouds. We show that strong anthropogenic cloud perturbations occur intermittently and only in the case of favourable meteorological conditions. We challenge the assumption of unidirectional increases in cloud thickness in contemporary climate models and show that the cloud thickness does not increase in response to aerosols on average. This indicates that the cooling effect of anthropogenic aerosols on Earth’s climate might not be as strong as previously assumed. Our results will ultimately lead to more reliable climate projections.https://www.ester.ee/record=b550810

Earth Observing System. Volume 1, Part 2: Science and Mission Requirements. Working Group Report Appendix

Areas of global hydrologic cycles, global biogeochemical cycles geophysical processes are addressed including biological oceanography, inland aquatic resources, land biology, tropospheric chemistry, oceanic transport, polar glaciology, sea ice and atmospheric chemistry

Physical science research needed to evaluate the viability and risks of marine cloud brightening

Marine cloud brightening (MCB) is the deliberate injection of aerosol particles into shallow marine clouds to increase their reflection of solar radiation and reduce the amount of energy absorbed by the climate system. From the physical science perspective, the consensus of a broad international group of scientists is that the viability of MCB will ultimately depend on whether observations and models can robustly assess the scale-up of local-to-global brightening in today\u27s climate and identify strategies that will ensure an equitable geographical distribution of the benefits and risks associated with projected regional changes in temperature and precipitation. To address the physical science knowledge gaps required to assess the societal implications of MCB, we propose a substantial and targeted program of research-field and laboratory experiments, monitoring, and numerical modeling across a range of scales

Shipping regulations lead to large reduction in cloud perturbations

Global shipping accounts for 13% of global emissions of SO2, which, once oxidized to sulfate aerosol, acts to cool the planet both directly by scattering sunlight and indirectly by increasing the albedo of clouds. This cooling due to sulfate aerosol offsets some of the warming effect of greenhouse gasses and is the largest uncertainty in determining the change in the Earth’s radiative balance by human activity. Ship tracks—the visible manifestation of the indirect of effect of ship emissions on clouds as quasi-linear features—have long provided an opportunity to quantify these effects. However, they have been arduous to catalog and typically studied only in particular regions for short periods of time. Using a machine-learning algorithm to automate their detection we catalog more than 1 million ship tracks to provide a global climatology. We use this to investigate the effect of stringent fuel regulations introduced by the International Maritime Organization in 2020 on their global prevalence since then, while accounting for the disruption in global commerce caused by COVID-19. We find a marked, but clearly nonlinear, decline in ship tracks globally: An 80% reduction in SOx emissions causes only a 25% reduction in the number of tracks detected

Security through societal resilience: Contemporary challenges in the Anthropocene

The concept of societal resilience has rapidly spread throughout the policy world, driven by the desire to use systems theories and process understandings to develop new security approaches for coping, bouncing-back, and adaptive improvement in the face of shocks and disturbances. However, this article argues that under the auspices of the Anthropocene, the assumptions and goals of societal resilience become problematic. This is because external interventions often ignore feedback effects, meaning that attempts to resolve problems through focusing upon enabling and capacity-building can be seen as counterproductive “fire-fighting” rather than tackling causation. Even more "alternative" or "community-based" approaches, relying upon interventions to enable so-called "natural" processes, either through an emphasis on local and traditional knowledge or new monitoring technologies, constitute problems for resilience advocacy: firstly, the problem of unrecognized exploitation; and secondly, the problem of continuing to sacrifice others to maintain unsustainable Western modes of consumption and production