Climate and Sustainability
Our Climate change is an incredibly urgent threat to humanity with far-reaching societal and economic consequences. Our research is advancing AI to empower partners and users to take climate mitigation action, and help communities adapt to the effects of climate change while building resilience. As part of our mission to build a safer, more sustainable future for all, we collaborate closely with cities, governments, startups and aid organizations.
Recent Publications
Global monitoring of methane point sources using deep learning on hyperspectral radiance measurements from EMIT
Michelangelo Conserva
Alex Wilson
Anna Michalak
Philip Brodrick
Andrew Thorpe
Proceedings of the National Academy of Sciences (PNAS) (2026)
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Anthropogenic methane (CH4) point sources are critical drivers of near-term climate forcing, safety hazards, and system inefficiencies. Global tracking with imaging spectroscopy is just becoming feasible, but still largely relies on manual tracking. Here we present the Methane Analysis and Plume Localization with EMIT (MAPL-EMIT) model, an end-to-end vision transformer framework that advances the state of the practice by directly utilizing the complete radiance spectrum from the Earth Surface Mineral Dust Source Investigation (EMIT) instrument to jointly retrieve methane enhancements across all pixels within a scene. This approach joins spectral information content - where the methane signature resides - with essential spatial context to significantly lower detection limits. MAPL-EMIT simultaneously supports quantification, plume delineation, and source localization, even for multiple overlapping plumes. The model was trained on 3.6 million physics-based synthetic plumes injected into global EMIT radiance data. Evaluation against synthetic observations confirms the model’s ability to identify plumes with high recall and precision and to capture weaker plumes relative to existing matched-filter approaches. On real-world benchmarks, MAPL-EMIT captures 79% of known hand-annotated NASA L2B plume complexes across a test set of 1084 EMIT granules, while identifying twice as many plausible plumes than identified by human analysts. Further validation against coincident airborne data, top-emitting landfills, and controlled release experiments confirms the models efficacy at identifying previously uncaptured sources. By incorporating model-generated metrics such as spectral fit scores and estimated noise levels, the framework can further limit false-positive rates. Overall, MAPL-EMIT enables high-throughput implementation on the full EMIT data catalog, shifting methane monitoring from labor-intensive workflows to a rapid, scalable paradigm for facility-level accountability.
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Identification of camera trap images by artificial intelligence and human experts produces similar multi-species occupancy models
Daniel Thornton
Travis King
Lucy Perera-Romero
Alissa Anderson
Rony Garcia-Anleu
Scott Fitkin
Carly Vynne
Journal of Applied Ecology (2026)
Preview abstract
The use of camera traps in ecology and conservation has expanded rapidly, but the time spent to accurately identify species in camera trap images remains a fundamental challenge that limits project scope and impact. Although artificial intelligence (AI) is often used to speed up image processing, a human review step is still standard practice to arrive at final species identifications. A potentially transformative next step is thus to remove humans entirely from the analysis chain and still produce accurate statistical models for subsequent inference across a wide diversity of species and regions.
Here, we compare the output of Bayesian multi-species occupancy models derived from a complete AI workflow (no human review of images) with a general species classifier (SpeciesNet) to those from an expert (human) workflow, using large-scale camera datasets from three study areas and two distinct and diverse mid-large mammal assemblages. We apply several pre- and post-processing steps to the AI workflow to improve model agreement. We perform a comprehensive model comparison, assessing agreement in identified species–environment relationships and rates of occupancy and detection, and similarity of spatial projections of occupancy.
We found that for most species of mammal, AI based models were remarkably similar to expert-based models, with some variability based on post-processing decisions. This agreement was robust, holding across multiple metrics of comparison (e.g. parameter estimates, precision, occupancy and detection rates), multiple study sites and at species and community levels. Similarity in model output occurred even in the presence of misclassification errors, suggesting our approach was resilient to some level of false negatives and positives. Substantial divergence in model output and subsequent inference, while rare, was most prevalent for rarely detected species.
Synthesis and applications: The use of a global AI classifier to identify species and reproducible pre- and post-processing decisions makes our approach broadly applicable and particularly beneficial for national and international monitoring programs that collect large amounts of photo data on threatened, at risk, or management sensitive species and wildlife communities. A fully automated workflow will allow such programs to progress more rapidly from photo collection to analysis, inference and decision-making.
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Assessing Global Flood Risk from Atmospheric Rivers through Physically Guided Machine Learning
Assaf Shmuel
Oleg Zlydenko
Martin Gauch
Colin Price
Scientific Reports (2026)
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Atmospheric rivers (ARs) are narrow corridors of concentrated moisture transport that play a crucial role in the global water cycle, delivering both beneficial rainfall and severe floods. Here, we develop a physically guided, explainable machine learning framework to predict flood occurrence during AR conditions worldwide by integrating AR characteristics with meteorological and topographic variables. The best model achieves a receiver operating characteristic area under the curve (ROC AUC) of 0.94, outperforming a logistic regression baseline at 0.81. Despite their limited footprint, we find that one third of large midlatitude floods occur under AR conditions, reflecting their disproportionate role in global flood risk. SHAP analysis highlights integrated vapor transport, precipitation, and elevation as dominant predictors. We show the non-linear amplification of flood risk under combined conditions of high soil moisture and persistent AR activity, underscoring the importance of antecedent wetness in modulating flood risk. Using consistent reanalysis inputs, we find that model-estimated high-flood-risk AR conditions increased globally by over 10% from 1980 to 2020 and shifted poleward. Validation on an independent satellite-based flood database shows comparable skill. We estimate that roughly 90% of the global population lives in regions that experience at least one AR annually, underscoring the broad societal relevance of AR dynamics. These findings highlight the value of physically guided machine learning for mapping and monitoring AR-related flood risk globally, offering actionable insights for preparedness and climate adaptation.
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Regional climate risk assessment from climate models using probabilistic machine learning
Rob Carver
Tapio Schneider
John Anderson
Fei Sha
Nature Machine Intelligence (2026)
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Accurate, actionable climate information at kilometer scales is crucial for robust natural hazard risk assessment and infrastructure planning. Simulating the impact of climate at these resolutions remains intractable, forcing reliance on downscaling, either physics-based or statistical methods which transform climate simulations from coarse to impact-relevant resolutions. It is essential to comprehensively capture the interdependency among climate processes of interest. However, current approaches, either lack the desired scalability or are bespoke to specific types of hazards. We introduce GenFocal, a step change in paradigm. GenFocal is a computationally efficient, general-purpose, end-to-end probabilistic, generative framework that gives rise to full probabilistic characterizations of complex climate processes interacting at fine spatiotemporal scales. When employed to assess risk in the present climate, GenFocal more faithfully projects the risk of extremes than state-of-the-art approaches including one recommended by the US Fifth National Climate Assessment. GenFocal produces plausible tracks of tropical cyclones, providing accurate statistics of their genesis and evolution, even when they are absent from the corresponding climate simulations. GenFocal also shows compelling results that are consistent with the literature on projecting climate impact in an extended time frame (five decades to one century). In short, GenFocal transforms the numerical computation into statistical knowledge bases that can be efficiently queried, thus opening up new possibilities of assessing the impacts of future climates at the spatio-temporal scales relevant to local and regional communities. We believe this work, as a starting point, establishes generative AI technology as an effective and potent paradigm for modeling complex, high-dimensional multivariate statistical correlations that have deterred precise quantification of climate risks associated with hazards such as wildfires, extreme heat, tropical cyclones, and flooding; thereby enabling the evaluation of adaptation strategies for affected populations and the built environment.
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Neural general circulation models for modeling precipitation
Stephan Hoyer
Dmitrii Kochkov
Janni Yuval
Ian Langmore
Science Advances (2026)
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Climate models struggle to accurately simulate precipitation, particularly extremes and the diurnal cycle. While hybrid models combining machine learning and physics have emerged with the premise of improving precipitation simulations, none have proven sufficiently skillful or stable enough to outperform existing models in simulating precipitation.
Here, we present the first hybrid model that is trained directly on precipitation observations. The model runs at 2.8 degrees resolution and is built on the differentiable NeuralGCM framework. This model is stable for decadal simulations and demonstrates significant improvements over existing GCMs, ERA5 reanalysis, and a Global Cloud-Resolving Model in simulating precipitation. Our approach yields reduced biases, a more realistic precipitation distribution, improved representation of extremes, and a more accurate diurnal cycle.
Furthermore, it outperforms the ECMWF ensemble for mid-range weather forecasting.
This advance paves the way for more reliable simulations of current climate and for the ability to fully utilize the abundance of existing observations to further improve GCMs.
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Preview abstract
The need for characterizing global variability of atmospheric carbon dioxide (CO2) is quickly increasing, with a growing urgency for tracking greenhouse gasses with sufficient resolution, precision and accuracy so as to support independent verification of CO2 fluxes at local to global scales. The current generation of space-based sensors, however, can only provide sparse observations in space and/or in time, by design. While upcoming missions may address some of these challenges, most are still years away from launch. This challenge has fueled interest in the potential use of data from existing missions originally developed for other applications for inferring global greenhouse gas variability. The Advanced Baseline Imager (ABI) onboard the Geostationary Operational Environmental Satellite (GOES-East), operational since 2017, provides full coverage of much of the western hemisphere at 10-minute intervals from geostationary orbit at 16 wavelengths at an approximately 2km2 spatial resolution. Here, we leverage this high temporal resolution by developing a single-pixel, fully connected neural network to estimate dry-air column CO2 mole fractions (XCO2). The model employs a time series of GOES-East's 16 spectral bands, ECMWF ERA5 lower tropospheric meteorology, MODIS surface reflectance, solar angles, and day of year. Training used collocated GOES-East and OCO-2/OCO-3 observations. The model is able to capture realistic XCO2 variability when compared against held-out years of OCO-2 observations and against observations from the TCCON network. We also present case studies illustrating the use of the model to observe XCO2 enhancements over urban areas, drawdown over agricultural regions, and enhanced ability to observe regions with persistent cloud cover thanks to GOES-East’s full spatial coverage and high observational frequency. Overall, although GOES-East derived XCO2 precision cannot rival that of dedicated instruments, its unprecedented combination of contiguous geographic coverage, 10-minute temporal frequency, and multi-year record offers the potential to observe aspects of atmospheric CO2 variability currently unseen from space.
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