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
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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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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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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Day-of-the-week Awareness in Time of Day Breakpoints for Traffic Light Plans
Ori Rottenstreich
Eliav Buchnik
Shai Ferster
Tom Kalvari
Ron Tsibulsky
Danny Veikherman
Jack Haddad
2025
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Time-of-day breakpoints (TODs) refer to the times over the day in which the plan of a traffic light is changed. Traditionally, TODs are selected jointly for all weekdays (Monday-Friday), typically with additional TODs dedicated to weekends. In this paper, we present an alternative approach motivated by traffic characteristics that can differ among the weekdays Monday-Friday and consider TODs which are day-of-the-week aware. The traffic-aware approach studies similarities among days and computes TODs that can be shared among days with similar characteristics but can also have other forms for weekdays with unique characteristics. Based on traffic properties derived from anonymized trajectories, we apply the new methodology to compute time-of-day breakpoints that are day-of-the-week aware in the city of Rio de Janeiro, Brazil and estimate the impact of the new methodology.
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Heterogeneous graph neural networks for species distribution modeling
Christine Kaeser-Chen
Keith Anderson
Michelangelo Conserva
Elise Kleeman
Maxim Neumann
Matt Overlan
Millie Chapman
Drew Purves
arxiv (2025)
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Species distribution models (SDMs) are necessary for measuring and predicting occurrences and habitat suitability of species and their relationship with environmental factors. We introduce a novel presence-only SDM with graph neural networks (GNN). In our model, species and locations are treated as two distinct node sets, and the learning task is predicting detection records as the edges that connect locations to species. Using GNN for SDM allows us to model fine-grained interactions between species and the environment. We evaluate the potential of this methodology on the six-region dataset compiled by National Center for Ecological Analysis and Synthesis (NCEAS) for benchmarking SDMs. For each of the regions, the heterogeneous GNN model is comparable to or outperforms previously-benchmarked single-species SDMs as well as a feed-forward neural network baseline model.
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Advancing seasonal prediction of tropical cyclone activity with a hybrid AI-physics climate model
Gan Zhang
Megha Rao
Janni Yuval
Ming Zhao
Environmental Research Letters (2025)
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Machine learning (ML) models are successful with weather forecasting and have shown progress in climate simulations, yet leveraging them for useful climate predictions needs exploration. Here we show this feasibility using neural general circulation model (NeuralGCM), a hybrid ML-physics atmospheric model developed by Google, for seasonal predictions of large-scale atmospheric variability and Northern Hemisphere tropical cyclone (TC) activity. Inspired by physical model studies, we simplify boundary conditions, assuming sea surface temperature and sea ice follow their climatological cycle but persist anomalies present at the initialization time. With such forcings, NeuralGCM can generate 100 simulation days in ∼8 min with a single graphics processing unit while simulating realistic atmospheric circulation and TC climatology patterns. This configuration yields useful seasonal predictions (July–November) for the tropical atmosphere and various TC activity metrics. Notably, the predicted and observed TC frequency in the North Atlantic and East Pacific basins are significantly correlated during 1990–2023 (r = ∼0.7), suggesting prediction skill comparable to existing physical GCMs. Despite challenges associated with model resolution and simplified boundary forcings, the model-predicted interannual variations demonstrate significant correlations with the observed sub-basin TC tracks (p < 0.1) and basin-wide accumulated cyclone energy (ACE) (p < 0.01) of the North Atlantic and North Pacific basins. These findings highlight the promise of leveraging ML models with physical insights to model TC risks and deliver seamless weather-climate predictions.
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