PUBLICATIONS, PAPERS + CONFERENCE

Acknowledgements

Acknowledging LEAP Support in Publications

All papers supported in full or in part by LEAP should acknowledge LEAP funding in the Acknowledgements section as follows:

“We acknowledge funding from NSF through the Learning the Earth with Artificial intelligence and Physics (LEAP) Science and Technology Center (STC) (Award #2019625).”

Center Publications

PEER REVIEWED PUBLICATIONS

Behrens, Gunnar, Tom Beucler, Pierre Gentine, et al. “Non-Linear Dimensionality Reduction With a Variational Encoder Decoder to Understand Convective Processes in Climate Models.” Journal of Advances in Modeling Earth Systems, vol. 14, no. 8, 2022, p. e2022MS003130, https://doi.org/10.1029/2022MS003130.
Bennington, Val, Tomislav Galjanic, et al. “Explicit Physical Knowledge in Machine Learning for Ocean Carbon Flux Reconstruction: The PCO2-Residual Method.” Journal of Advances in Modeling Earth Systems, vol. 14, no. 10, 2022, p. e2021MS002960, https://doi.org/10.1029/2021MS002960.
Bennington, Val, Lucas Gloege, et al. “Variability in the Global Ocean Carbon Sink From 1959 to 2020 by Correcting Models With Observations.” Geophysical Research Letters, vol. 49, no. 14, 2022, p. e2022GL098632, https://doi.org/10.1029/2022GL098632.
Beucler, Tom, et al. “Climate-Invariant Machine Learning.” Science Advances, vol. 10, no. 6, Feb. 2024, p. eadj7250, https://doi.org/10.1126/sciadv.adj7250.
Bhouri, Mohamed Aziz, and Pierre Gentine. “Memory-Based Parameterization with Differentiable Solver: Application to Lorenz ’96.” Chaos: An Interdisciplinary Journal of Nonlinear Science, vol. 33, no. 7, July 2023, p. 073116, https://doi.org/10.1063/5.0131929.

Bu, Jingyi, et al. “Dryland Evapotranspiration from Remote Sensing Solar-Induced Chlorophyll Fluorescence: Constraining an Optimal Stomatal Model within a Two-Source Energy Balance Model.” Remote Sensing of Environment, vol. 303, Mar. 2024, p. 113999, https://doi.org/10.1016/j.rse.2024.113999.

Buch, J., Williams, A., Juang, C., Hansen, W., & Gentine, P. (2023). SMLFire1.0: a stochastic machine learning (SML) model for wildfire activity in the western United States. Geoscientific Model Development, 16(12), 3407-3433. http://dx.doi.org/10.5194/gmd-16-3407-2023.

Chen, Tse-Chun, et al. “Correcting Systematic and State-Dependent Errors in the NOAA FV3-GFS Using Neural Networks.” Journal of Advances in Modeling Earth Systems, vol. 14, no. 11, 2022, p. e2022MS003309, https://doi.org/10.1029/2022MS003309.

Cheng, Y., Giometto, M. G., Kauffmann, P., Lin, L., Cao, C., Zupnick, C., Abernathey, R. & Gentine, P. Deep learning for subgrid-scale turbulence modeling in large-eddy simulations of the convective atmospheric boundary layer. Journal of Advances in Modeling Earth Systems, 14, e2021MS002847, May 2022, https://doi.org/10.1029/2021MS002847.

Eidhammer, Trude & Gettelman, Andrew & Thayer-Calder, Katherine & Watson-Parris, Duncan & Elsaesser, Gregory & Morrison, Hugh & van Lier-Walqui, Marcus & Song, Ci & Mccoy, Daniel. (2024). An extensible perturbed parameter ensemble for the Community Atmosphere Model version 6. Geoscientific Model Development. 17. 7835-7853. https://doi.org/10.5194/gmd-17-7835-2024.

Eyring, V., Gentine, P., Camps-Valls, G. et al. AI-empowered next-generation multiscale climate modelling for mitigation and adaptation. Nat. Geosci. 17, 963–971 (2024). https://doi.org/10.1038/s41561-024-01527-w.

Eyring, V., Collins, W.D., Gentine, P. et al. Pushing the frontiers in climate modelling and analysis with machine learning. Nat. Clim. Chang. 14, 916–928 (2024). https://doi.org/10.1038/s41558-024-02095-y.

Heimdal, T.H., McKinley, G.A. The importance of adding unbiased Argo observations to the ocean carbon observing system. Sci Rep 14, 19763 (2024). https://doi.org/10.1038/s41598-024-70617-x.

Islam, Ariful, et al. Enhancing Satellite Data Coverage: Leveraging Multiple Sensors to Bridge Information Gaps in Urban Flood Mapping. AGU, 2023, https://agu.confex.com/agu/fm23/meetingapp.cgi/Paper/1353707.

Giardina, Francesco, et al. Groundwater Rivals Aridity in Determining Global Photosynthesis. 13 Mar. 2024, https://doi.org/10.21203/rs.3.rs-3793488/v1.

Grundner, Arthur, Tom Beucler, Pierre Gentine, and Veronika Eyring. “Data-Driven Equation Discovery of a Cloud Cover Parameterization.” Journal of Advances in Modeling Earth Systems, vol. 16, no. 3, 2024, p. e2023MS003763, https://doi.org/10.1029/2023MS003763.

Grundner, Arthur, Tom Beucler, Pierre Gentine, Fernando Iglesias-Suarez, et al. “Deep Learning Based Cloud Cover Parameterization for ICON.” Journal of Advances in Modeling Earth Systems, vol. 14, no. 12, 2022, p. e2021MS002959, https://doi.org/10.1029/2021MS002959.

Iglesias-Suarez, Fernando, et al. “Causally-Informed Deep Learning to Improve Climate Models and Projections.” Journal of Geophysical Research: Atmospheres, vol. 129, no. 4, 2024, p. e2023JD039202, https://doi.org/10.1029/2023JD039202.

Improving the Modeling and Analysis of Tropical Convection and Precipitation Through Machine Learning Methods – ProQuest. https://www.proquest.com/openview/b21c84cc107ccd37cba0f0a7e7c0c30f/1?pq-origsite=gscholar&cbl=18750&diss=y Accessed 15 Apr. 2024.

Li, Xing, et al. “New-Generation Geostationary Satellite Reveals Widespread Midday Depression in Dryland Photosynthesis during 2020 Western U.S. Heatwave.” Science Advances, vol. 9, no. 31, Aug. 2023, p. eadi0775, https://doi.org/10.1126/sciadv.adi0775.

Lockwood, J. W., Gori, A., & Gentine, P. (2024). A generative super-resolution model for enhancing tropical cyclone wind field intensity and resolution. Journal of Geophysical Research: Machine Learning and Computation, 1, e2024JH000375. https://doi.org/10.1029/2024JH000375.

McKinley, Galen A., et al. “Modern Air-Sea Flux Distributions Reduce Uncertainty in the Future Ocean Carbon Sink.” Environmental Research Letters, vol. 18, no. 4, Mar. 2023, p. 044011, https://doi.org/10.1088/1748-9326/acc195.

Mooers, Griffin, Mike Pritchard, et al. “Comparing Storm Resolving Models and Climates via Unsupervised Machine Learning.” Scientific Reports, vol. 13, no. 1, Dec. 2023, p. 22365, https://doi.org/10.1038/s41598-023-49455-w.

Mooers, Griffin S., et al. “An Unsupervised Learning Perspective on the Dynamic Contribution to Extreme Precipitation Changes.” Climate Change AI, Climate Change AI, 2022, https://www.climatechange.ai/papers/neurips2022/81.

Nathaniel, Juan, Jiangong Liu, et al. “MetaFlux: Meta-Learning Global Carbon Fluxes from Sparse Spatiotemporal Observations.” Scientific Data, vol. 10, no. 1, July 2023, p. 440, https://doi.org/10.1038/s41597-023-02349-y.

Pla, Paula, et al. “Hydrogenation of C24 Carbon Clusters: Structural Diversity and Energetic Properties.” The Journal of Physical Chemistry A, vol. 125, no. 24, June 2021, pp. 5273–88, https://doi.org/10.1021/acs.jpca.1c02359.

Portocarrero, Florencio F., and Vanessa C. Burbano. “The Effects of a Short-Term Corporate Social Impact Activity on Employee Turnover: Field Experimental Evidence.” Management Science, Oct. 2023, https://doi.org/10.1287/mnsc.2022.01517.

Schmidt, Gavin. A., et al. “Anomalous Meltwater From Ice Sheets and Ice Shelves Is a Historical Forcing.” Geophysical Research Letters, vol. 50, no. 24, 2023, p. e2023GL106530, https://doi.org/10.1029/2023GL106530.

Shamekh, Sara, et al. “Implicit Learning of Convective Organization Explains Precipitation Stochasticity.” Proceedings of the National Academy of Sciences, vol. 120, no. 20, May 2023, p. e2216158120, https://doi.org/10.1073/pnas.2216158120.

Shen, Chaopeng, et al. “Differentiable Modeling to Unify Machine Learning and Physical Models and Advance Geosciences.” Nature Reviews Earth & Environment, vol. 4, no. 8, July 2023, pp. 552–67, https://doi.org/10.1038/s43017-023-00450-9.

Skulovich, Olya, and Pierre Gentine. “A Long-Term Consistent Artificial Intelligence and Remote Sensing-Based Soil Moisture Dataset.” Scientific Data, vol. 10, no. 1, Mar. 2023, p. 154, https://doi.org/10.1038/s41597-023-02053-x.

Wong, S. C., McKinley, G. A., & Seager, R. Equatorial Pacific pCO2 interannual variability in CMIP6 models. Journal of Geophysical Research: Biogeosciences, e2022JG007243, Dec. 2022, https://doi.org/10.1029/2022JG007243.

Yang, Qingyuan, and Susanna F. Jenkins. “Two Sources of Uncertainty in Estimating Tephra Volumes from Isopachs: Perspectives and Quantification.” Bulletin of Volcanology, vol. 85, no. 8, July 2023, p. 44, https://doi.org/10.1007/s00445-023-01652-1.

Yu, Sungduk, Walter Hannah, Liran Peng, Jerry Lin, Mike Pritchard, et al. “ClimSim: A Large Multi-Scale Dataset for Hybrid Physics-ML Climate Emulation.” Advances in Neural Information Processing Systems, vol. 36, Dec. 2023, pp. 22070–84, https://proceedings.neurips.cc/paper_files/paper/2023/hash/45fbcc01349292f5e059a0b8b02c8c3f-Abstract-Datasets_and_Benchmarks.html

Zhan, W., Yang, X., Ryu, Y., Dechant, B., Huang, Y., Goulas, Y., … & Gentine, P. Two for one: Partitioning CO2 fluxes and understanding the relationship between solar-induced chlorophyll fluorescence and gross primary productivity using machine learning. Agricultural and Forest Meteorology, 321, 108980, Jun. 2022, https://doi.org/10.1016/j.agrformet.2022.108980.

Zhao, Wenli, Biqing Zhu, Steven J. Davis, Philippe Ciais, Chaopeng Hong, Zhu Liu and Pierre Gentine. “Reliance on Fossil Fuels Increases during Extreme Temperature Events in the Continental United States.” Communications Earth & Environment, vol. 4, no. 1, Dec. 2023, pp. 1–11, https://doi.org/10.1038/s43247-023-01147-z.

NON-PEER REVIEWED PUBLICATIONS

Agonafir, Candace, and Tian Zheng. “Structured Exploration of Machine Learning Model Complexity for Spatio-Temporal Forecasting of Urban Flooding.” EGUsphere, Mar. 2024, pp. 1–32, https://doi.org/10.5194/egusphere-2024-551.
Behrens, Gunnar, Tom Beucler, Fernando Iglesias-Suarez, et al. Improving Atmospheric Processes in Earth System Models with Deep Learning Ensembles and Stochastic Parameterizations. arXiv:2402.03079, arXiv, 5 Feb. 2024, https://doi.org/10.48550/arXiv.2402.03079.
Bhouri, Mohamed Aziz, et al. Multi-Fidelity Climate Model Parameterization for Better Generalization and Extrapolation. arXiv:2309.10231, arXiv, 18 Sept. 2023, https://doi.org/10.48550/arXiv.2309.10231.
Bhouri, Mohamed Aziz, and Pierre Gentine. History-Based, Bayesian, Closure for Stochastic Parameterization: Application to Lorenz ’96. arXiv:2210.14488, arXiv, 26 Oct. 2022, https://doi.org/10.48550/arXiv.2210.14488.
Hoffman, Andrew O., et al. “Inland Migration of Near-Surface Crevasses in the Amundsen Sea Sector, West Antarctica.” EGUsphere, Jan. 2024, pp. 1–29, https://doi.org/10.5194/egusphere-2023-2956.

Karpatne, Anuj, Xiaowei Jia, and Vipin Kumar (2024). Knowledge-guided Machine Learning: Current Trends and Future Prospects. arXiv:2403.15989, arXiv, 24 Mar. 2024, https://doi.org/10.48550/arXiv.2403.15989.

Kodner, Jordan, et al. Why Linguistics Will Thrive in the 21st Century: A Reply to Piantadosi (2023). arXiv:2308.03228, arXiv, 6 Aug. 2023, https://doi.org/10.48550/arXiv.2308.03228.

Lamb, Kara, Marcus van Lier Walqui, Sean Santos, Hugh Morrison. Reduced Order Modeling for Linear Representations of Microphysical Process Rates.
https://essopenarchive.org/users/553026/articles/653967-reduced-order-modeling-for-linearized-representations-of-microphysical-process-rates.

Langlois, Gabriel P., et al. Efficient First-Order Algorithms for Large-Scale, Non-Smooth Maximum Entropy Models with Application to Wildfire Science. arXiv:2403.06816, arXiv, 11 Mar. 2024, https://doi.org/10.48550/arXiv.2403.06816.

Liao, Kyleen, et al. Simulating the Air Quality Impact of Prescribed Fires Using a Graph Neural Network-Based PM$_{2.5}$ Emissions Forecasting System. arXiv:2312.04291, arXiv, 7 Dec. 2023, https://doi.org/10.48550/arXiv.2312.04291.

Lin, Jerry, et al. Stress-Testing the Coupled Behavior of Hybrid Physics-Machine Learning Climate Simulations on an Unseen, Warmer Climate. arXiv:2401.02098, arXiv, 4 Jan. 2024, https://doi.org/10.48550/arXiv.2401.02098.

Lin, Jerry, et al. Systematic Sampling and Validation of Machine Learning-Parameterizations in Climate Models. arXiv:2309.16177, arXiv, 28 Sept. 2023, https://doi.org/10.48550/arXiv.2309.16177.

Mooers, Griffin, Tom Beucler, et al. Understanding Extreme Precipitation Changes through Unsupervised Machine Learning. arXiv:2211.01613, arXiv, 1 Dec. 2023, https://doi.org/10.48550/arXiv.2211.01613.

PythonicDISORT: A Python Reimplementation of the Discrete Ordinate Radiative TransferPackage DISORT. Journal of Open Source Software, https://joss.theoj.org/papers/ec19f2796dac7303ea3bd476455b641f. Accessed 22 Apr. 2024.

Nathaniel, Juan, Yongquan Qu, et al. ChaosBench: A Multi-Channel, Physics-Based Benchmarkfor Subseasonal-to-Seasonal Climate Prediction. arXiv:2402.00712, arXiv, 1 Mar. 2024, https://doi.org/10.48550/arXiv.2402.00712.

Qu, Yongquan, Juan Nathaniel, Shuolin Li, Pierre Gentine. Deep Generative Data Assimilation in Multimodal Setting. arXiv:2404.06665, arXiv, 9 Apr. 2024, https://doi.org/10.48550/arXiv.2404.06665.

Qu, Yongquan, Mohamed Aziz Bhouri, Pierre Gentine. Joint Parameter and Parameterization Inference with Uncertainty Quantification through Differentiable Programming. 2024, https://doi.org/10.48550/ARXIV.2403.02215.

Renganathan, Arvind, et al. Task Aware Modulation Using Representation Learning: An Approach for Few Shot Learning in Heterogeneous Systems. arXiv:2310.04727, arXiv, 7 Oct. 2023, http://arxiv.org/abs/2310.04727.

Shamekh, Sara, P. Gentine. Learning Atmospheric Boundary Layer Turbulence. 23 Jun. 2023. https://essopenarchive.org/doi/full/10.22541/essoar.168748456.60017486.

Shi, Haiyang. Global Pattern and Mechanism of Terrestrial Evapotranspiration Change Indicated by Weather Stations. arXiv:2309.06822, arXiv, 13 Sept. 2023, https://doi.org/10.48550/arXiv.2309.06822.

Will, Justus C., Andrea M. Jenney, Kara D. Lamb, Michael S. Pritchard, Colleen Kaul, Po-Lun Ma, Kyle Pressel, Jacob Shpund, Marcus van Lier-Walqui, Stephan Mandt. Understanding and Visualizing Droplet Distributions in Simulations of Shallow Clouds. In Machine Learning and the Physical Sciences Workshop, Neural Information Processing Conference 2023. https://arxiv.org/abs/2310.20168.

Xi, Xuan. Pierre Gentine, Qianlai Zhuang and Seungbum Kim. Evaluating the Effects of Precipitation and Evapotranspiration on Soil Moisture Variability. https://essopenarchive.org/doi/full/10.1002/essoar.10511220.1. Accessed 23 Apr. 2024.

Yu, Sungduk, Walter Hannah, Liran Peng, Jerry Lin, Michael Pritchard, et al. ClimSim: A Large Multi-Scale Dataset for Hybrid Physics-ML Climate Emulation. arXiv:2306.08754, arXiv, 6 Feb. 2024, https://doi.org/10.48550/arXiv.2306.08754.

Yu, Sungduk et al. “ClimSim-Online: A Large Multi-scale Dataset and Framework for Hybrid ML-physics Climate Emulation.” 8 Jul. 2024, https://doi.org/10.48550/arXiv.2306.08754.

Synergistic Publications

PEER REVIEWED PUBLICATIONS

Agonafir, Candace, et al. “A Review of Recent Advances in Urban Flood Research.” Water Security, vol. 19, Aug. 2023, p. 100141, https://doi.org/10.1016/j.wasec.2023.100141.
Biass, Sébastien, et al. “How Well Do Concentric Radii Approximate Population Exposure to Volcanic Hazards?” Bulletin of Volcanology, vol. 86, no. 1, Dec. 2023, p. 3, https://doi.org/10.1007/s00445-023-01686-5.
Burbano, Vanessa C., et al. “The Gender Gap in Meaningful Work.” Management Science, Dec. 2023, https://doi.org/10.1287/mnsc.2022.01807.
Burbano, Vanessa, et al. “The Past and Future of Corporate Sustainability Research.” Organization & Environment, Articles in Advance, Dec. 2023, pp.1-20, https://doi.org/10.1177/10860266231213105.
Camps-Valls, Gustau, Andreas Gerhardus, Urmi Ninad, Gherardo Varando, Georg Martius, Emili Balaguer-Ballester, Ricardo Vinuesa, Emiliano Diaz, Laure Zanna, Jakob Runge. “Discovering Causal Relations and Equations from Data.” Physics Reports, vol. 1044, Dec. 2023, pp. 1–68, https://doi.org/10.1016/j.physrep.2023.10.005.

Chang, Chiung-Yin, et al. “Remote Versus Local Impacts of Energy Backscatter on the North Atlantic SST Biases in a Global Ocean Model.” Geophysical Research Letters, vol. 50, no. 21, 2023, p. e2023GL105757, https://doi.org/10.1029/2023GL105757.

Falasca, Fabrizio, et al. “Data-Driven Dimensionality Reduction and Causal Inference for Spatiotemporal Climate Fields.” Physical Review E, vol. 109, no. 4, Apr. 2024, p. 044202, https://doi.org/10.1103/PhysRevE.109.044202.

Friedlingstein, Pierre, et al. “Global Carbon Budget 2023.” Earth System Science Data, vol. 15, no. 12, Dec. 2023, pp. 5301–69, https://doi.org/10.5194/essd-15-5301-2023.

Gregory, William, et al. “Deep Learning of Systematic Sea Ice Model Errors From DataAssimilation Increments.” Journal of Advances in Modeling Earth Systems, vol. 15, no. 10, 2023, p. e2023MS003757, https://doi.org/10.1029/2023MS003757.

Gregory, William, et al. “Machine Learning for Online Sea Ice Bias Correction Within Global Ice-Ocean Simulations.” Geophysical Research Letters, vol. 51, no. 3, 2024, p.e2023GL106776, https://doi.org/10.1029/2023GL106776.

Heimdal, Thea Hatlen, et al. “Assessing Improvements in Global Ocean PCO2 Machine Learning Reconstructions with Southern Ocean Autonomous Sampling.” Biogeosciences Discussions, Oct. 2023, pp. 1–35, https://doi.org/10.5194/bg-2023-160.

Hu, Zeyuan, Akshay Subramaniam, Zhiming Kuang, Jerry Lin, Sungduk Yu, Walter M. Hannah, Noah D. Brenowitz, Josh Romero and Michael S. Pritchard. “Stable Machine-Learning Parameterization of Subgrid Processes with Real Geography and Full-physics Emulation.” 5 Aug. 2024, https://doi.org/10.48550/arXiv.2407.00124.

Lamb, K. D., and P. Gentine. “Zero-Shot Learning of Aerosol Optical Properties with Graph Neural Networks.” Scientific Reports, vol. 13, no. 1, Oct. 2023, p. 18777, https://doi.org/10.1038/s41598-023-45235-8.

Newsom, Emily, et al. “Background Pycnocline Depth Constrains Future Ocean Heat Uptake Efficiency.” Geophysical Research Letters, vol. 50, no. 22, 2023, p. e2023GL105673, https://doi.org/10.1029/2023GL105673.

Olivarez, Holly C., et al. “How Does the Pinatubo Eruption Influence Our Understanding of Long-Term Changes in Ocean Biogeochemistry?” Geophysical Research Letters, vol. 51, no. 2, 2024, p. e2023GL105431, https://doi.org/10.1029/2023GL105431.

Sane, Aakash, et al. “Parameterizing Vertical Mixing Coefficients in the Ocean Surface Boundary Layer Using Neural Networks.” Journal of Advances in Modeling Earth Systems, vol. 15, no. 10, 2023, p. e2023MS003890, https://doi.org/10.1029/2023MS003890.

Zhang, Cheng, et al. “Implementation and Evaluation of a Machine Learned Mesoscale Eddy Parameterization Into a Numerical Ocean Circulation Model.” Journal of Advances in Modeling Earth Systems, vol. 15, no. 10, 2023, p. e2023MS003697, https://doi.org/10.1029/2023MS003697.

NON-PEER REVIEWED PUBLICATIONS

Bodner, Abigail, et al. A Data-Driven Approach for Parameterizing Submesoscale VerticalBuoyancy Fluxes in the Ocean Mixed Layer. arXiv:2312.06972, arXiv, 11 Dec. 2023, https://doi.org/10.48550/arXiv.2312.06972.
Heimdal, T.H. and G.A. McKinley (2024) Using observing system simulation experiments to assess impacts of observational uncertainties in surface ocean pCO2 machine learning reconstructions, Scientific Rep., in review.
Hermans, Tim H. J., et al. Projecting Changes in the Drivers of Compound Flooding in Europe Using CMIP6 Models. 27 Oct. 2023, https://doi.org/10.22541/essoar.169841704.46464014/v1.
Langlois, Gabriel P., et al. Efficient First-Order Algorithms for Large-Scale, Non-Smooth Maximum Entropy Models with Application to Wildfire Science. arXiv:2403.06816, arXiv, 11 Mar. 2024, https://doi.org/10.48550/arXiv.2403.06816.
Pedersen, Christian, et al. Reliable Coarse-Grained Turbulent Simulations through Combined Offline Learning and Neural Emulation. arXiv:2307.13144, arXiv, 24 July 2023, https://doi.org/10.48550/arXiv.2307.13144.

Perezhogin, Pavel, et al. Implementation of a Data-Driven Equation-Discovery Mesoscale Parameterization into an Ocean Model. arXiv:2311.02517, arXiv, 4 Nov. 2023, https://doi.org/10.48550/arXiv.2311.02517.

Books + Book Chapters

Beucler, Tom, et al. “Machine Learning for Clouds and Climate.” Clouds and Their Climatic Impacts, American Geophysical Union (AGU), 2023, pp. 325–45, https://doi.org/10.1002/9781119700357.ch16.
Reyes, Nicole Alia Salis, et al. “(Re)Wiring Settler Colonial Practices in Higher Education: Creating Indigenous Centered Futures Through Considerations of Power, the Social, Place, and Space.” Higher Education: Handbook of Theory and Research: Volume 39, edited by Laura W. Perna, Springer Nature Switzerland, 2024, pp. 187–263, https://doi.org/10.1007/978-3-031-38077-8_5.

Conferences

Accarino, Gabriele, et al. WaveSim: A Multi-Scale Wavelet-Based Similarity Metric for Climate Field Comparison. AGU, 2025. https://agu.confex.com/agu/agu25/meetingapp.cgi/Paper/1950455.
Acquaviva, Viviana, Barnes, Elizabeth, Gagne, John David II, McKinley, Galen, Thais, Savannah, “Ethics and Explainability in Climate AI: from theory to practice), panel discussion, Climate Informatics 2024. https://alan-turing-institute.github.io/climate-informatics-2024/schedule/.
Acquaviva, Viviana, “From ML x Astrophysics to ML x Climate: A journey across disciplines”, PIVOT fellowship symposium, April 2024.
Agonafir, Candace, et al. Assessing Reproducibility Standards in Earth Science: Insights from LEAP Lab Research. AGU, 2024. https://agu.confex.com/agu/agu24/meetingapp.cgi/Paper/1614014.
Blanke, Matthieu, et al. Physically-Constrained Deep Generative Modeling. AGU, 2025. https://agu.confex.com/agu/agu25/meetingapp.cgi/Paper/1992938.
Buch, Jatan, et al. Optimizing cloud seeding with a denoising diffusion model. AGU, 2024. https://agu.confex.com/agu/agu24/meetingapp.cgi/Paper/1580482.
Camara, Fatoumata, Agonafir, Candace, and Zheng, Tian. Visualizing Flood Impacts In NYC Neighborhoods Due to Climate Change. AGU, 2024. https://agu.confex.com/agu/agu24/meetingapp.cgi/Paper/1734020.
Dagon, Katherine, et al. Perturbed Parameter Ensembles (PPEs) for Understanding Processes and Quantifying Uncertainty in Earth System Models I Oral. AGU, 2023, https://agu.confex.com/agu/fm23/meetingapp.cgi/Session/202455.
Elsaesser, Gregory, et al. “Using Machine Learning to Generate a NASA GISS ESM Calibrated Physics Ensemble”, AGU, 2023, https://agu.confex.com/agu/fm23/meetingapp.cgi/Paper/1437342.
Elsaesser, Gregory, and. M. McGraw. AI Advances in Tropical Meteorology: Tropical Cyclones, Sub-Seasonal Phenomena, and More. AMS, 2024, https://ams.confex.com/ams/104ANNUAL/meetingapp.cgi/Session/65400.
Elsaesser, Greg, et al. Perturbed Parameter Ensembles (PPEs) for Understanding Processes and Quantifying Uncertainty in Earth System Models. AGU, 2024. https://agu.confex.com/agu/agu24/meetingapp.cgi/Session/228865.
Elsaesser, Greg, and van Lier-Walqui, Marcus. A Climate Model Calibrated Physics Ensemble (CPE) As One Foundation For Climate Observing System Simulation Experiments. AGU, 2024. (invited) https://agu.confex.com/agu/agu24/meetingapp.cgi/Paper/1550424.
Elsaesser Greg, et al. Perturbed Parameter Ensembles (PPEs) for Understanding Processes and Quantifying Uncertainty in Earth System Models. AGU, 2024. https://agu.confex.com/agu/agu24/meetingapp.cgi/Session/234018.
Fan, Da, Gagne II, David John, et al. Uncertainty Estimation and Explanation for Convective Initiation Nowcasting Using Bayesian Deep Learning. AGU, 2025. https://agu.confex.com/agu/agu25/meetingapp.cgi/Paper/1990433.
Fan, Da, et al. Exploring Structural Differences and Parameter Calibration for Microphysics in CESM with Latent Representations using Contrastive Learning Models. AGU, 2025. https://agu.confex.com/agu/agu25/meetingapp.cgi/Paper/1989947.
Fan, Da, et al. Disentangled Contrastive Representation Learning for Interpretable Analysis of Warm Rain Microphysics Schemes. AGU, 2025. https://agu.confex.com/agu/agu25/meetingapp.cgi/Paper/2002027.
Fay, Amanda, et al. Scale-dependent drivers of air-sea CO₂ flux variability using the ECCO-Darwin model. AGU, 2024. https://agu.confex.com/agu/agu24/meetingapp.cgi/Paper/1680752.
Florence, Kaleb, et al. Detecting nonlinear causal relationships between El Niño and soil moisture in tropical forests. AGU, 2025. https://agu.confex.com/agu/agu25/meetingapp.cgi/Paper/1976298.
Frields, Katherine, et al. Determining Thermodynamically Relevant Morphological Features of Cirrus Ice Crystals Using a Convolutional Neural Network. AGU, 2025. https://agu.confex.com/agu/agu25/meetingapp.cgi/Paper/1983671.
Gentine, Pierre. Differentiable land-surface model. Cornell University 6th Training Course on New Advances in Land Carbon Cycle Modeling. Cornell University, May 2023 (invited) https://ecolab.cals.cornell.edu/?training_course_2023.
Gentine, Pierre. How can AI help with climate adaptation and resilience? United Nations Geneva, July 2023. (invited)

Gentine, Pierre; LEAPing across scales for climate modeling. Multiscale Physics Symposium. Simons Foundation, August 2023. (invited) https://www.simonsfoundation.org/event/multi-scale-physics-2023/.

Gentine, Pierre, and Bhouri, M. Aziz. Multifidelity climate model parameterizations. AGU, 2023. (invited) https://agu.confex.com/agu/fm23/meetingapp.cgi/Paper/1290385.

Gentine, Pierre, and Nathaniel Juan. Intrinsic dimension of spatio-temporal chaotic systems. AGU, 2023. (invited) https://agu.confex.com/agu/fm23/meetingapp.cgi/Paper/1378633.

Gentine, Pierre. Next generation climate models with new tools. ETH Zurich, December 2023.

Gentine, Pierre. AI for weather and climate forecasting. EESI, Congress AI bill on AI for weather. February 2024. (invited) https://www.eesi.org/briefings/view/021524weather.

Gentine, Pierre. AI for climate: from emulation to new discoveries. Simons Foundation Presidential Lecture. March 2024. (invited) https://www.simonsfoundation.org/event/ai-in-climate-science-from-emulation-to-new-discoveries/#:~:text=Simulating%20Earth’s%20climate%20is%20a,tool%20in%20 overcoming%20 these%20 roadblocks.

Gentine, Pierre. Machine learning in climate science, from emulation to discoveries. Berkeley Atmospheric Sciences Center, UC Berkeley. March 2024 (invited) https://atmosphere.berkeley.edu/symposium2024.php.

Gentine, Pierre. Next generation climate models. Stanford University, March 2024. (invited)

Gentine, Pierre. Interdependencies between migration and climate: a need for a transdisciplinary Earth System approach. Workshop on Climate Change and Human Migration: An Earth Systems Science Perspective. National Academies, March 2024. (invited) https://www.nationalacademies.org/event/41814_03-2024_workshop-on-climate-change-and-human-migration-an-earth-systems-science-perspective.

Gentine, Pierre. Keynote: Harnessing AI to Confront Environmental Challenges. Bezos Earth Fund AI For Climate and Nature Spring Convening. Columbia University, April 2024. https://www.climate.columbia.edu/events/ai-climate-and-nature-spring-convening.

Gentine, Pierre. Machine learning for climate science: from emulation to new discoveries. Seoul National University, April 2024 (invited) https://calslab.snu.ac.kr/agronomy/board.read?mcode=1910&id=188

Gentine, Pierre, V. Eyring, M. Reichstein,G. Camps-Valls, Gudstau. Pushing Frontier Research in Climate Modelling and Understanding with AI for Urgent Mitigation and Adaptation Needs. United Nations, May 2024.

Gentine, Pierre. Climate model refactorization to JAX. Microsoft, May 2024

Gentine, Pierre, and Nathaniel, Juan. Can ML beats chaos? AGU, 2024. https://agu.confex.com/agu/agu24/meetingapp.cgi/Paper/1522150.

Gentine, Pierre, et al. Parsimony versus complexity. AGU, 2025 (invited). https://agu.confex.com/agu/agu25/meetingapp.cgi/Paper/1859486.

Hafner, Katharina, et al. Interpretable Machine Learning-based Radiation Emulation for ICON. AGU, 2024. https://agu.confex.com/agu/agu24/meetingapp.cgi/Paper/1562645.

Heimdal, Thea H., et al. Using sub-sampling experiments to better understand the ocean carbon sink. AGU, 2024. https://agu.confex.com/agu/agu24/meetingapp.cgi/Paper/1604374.

Hu, Arthur, et al. How well can power laws describe microphysical processes in bulk schemes? AGU, 2024. https://agu.confex.com/agu/agu24/meetingapp.cgi/Paper/1755949.

Jung, Jaeyoung, et al. Canopy Flow Modeling Using Multiscale Homogenization Techniques. AGU, 2023, https://agu.confex.com/agu/fm23/meetingapp.cgi/Paper/1444637.

Jung, Jaeyoung, et al. Data-driven multiscale modeling of flow in plant canopies. AGU, 2024. https://agu.confex.com/agu/agu24/meetingapp.cgi/Paper/1664073.

Ko, Joseph, et al. Classification of Cloud Particle Imagery Using Variational Autoencoders and Unsupervised Clustering. AMS, 2024, https://ams.confex.com/ams/104ANNUAL/meetingapp.cgi/Paper/432580.

Ko, Joseph, et al. Informing Depositional Ice Growth Models Through 3-D Reconstruction of Ice Crystal Images Using Machine Learning. AMS, 2024, https://ams.confex.com/ams/104ANNUAL/meetingapp.cgi/Paper/432551.

Ko, Joseph, et al. Exploring Ice Microphysical Bulk Parameter Sensitivities with Perturbed Parameter Ensembles of Single Column Models. AGU, 2025. https://agu.confex.com/agu/agu25/meetingapp.cgi/Paper/1977669.

Ko, Joseph, et al. Understanding Ice Crystal Habit Diversity with Self-Supervised Learning. AGU, 2025. https://agu.confex.com/agu/agu25/meetingapp.cgi/Paper/1984538.

Kumar, Sudhanshu, et al. Leveraging Machine Learning and MCMC to Optimize Community Land Model Parameters for Accurate Gross Primary Productivity Estimates. AGU, 2025. https://agu.confex.com/agu/agu25/meetingapp.cgi/Paper/1882489.

Kumar, Vipin. Role of Big Data and Machine Learning for Addressing Global Environmental Challenges. AAAI 2023 Fall Symposium Series. https://www.climatechange.ai/events/aaaifss2023#schedule

Kumar, Vipin, et al. Knowledge-guided Machine Learning for Modeling Multi-scale Processes and Data Assimilation: An Application to Streamflow Forecasting for River Basins. AGU, 2024. https://agu.confex.com/agu/agu24/meetingapp.cgi/Paper/1703490.

Lahlou, Aya, et al. Global Characterization and Forecasting of Leaf Phenology using Deep Learning Methods and Remote Sensing. https://agu.confex.com/agu/agu24/meetingapp.cgi/Paper/1722179.

Lahlou, Aya, et al. Integrating Transformer-Based Land Surface Phenology into the Community Land Model for Climate-Responsive Vegetation Dynamics. AGU, 2025. https://agu.confex.com/agu/agu25/meetingapp.cgi/Paper/2003866.

Lamb, Kara D. J. Mikhaeil, J. Harrington, M. van Lier Walqui. Cloud chamber constraints on depositional ice growth models. AGU Fall Meeting 2023. San Francisco, CA, December 2023.

Lamb, Kara D. and P. Gentine. Exploring Phase Transitions and Dynamical Processes in Tropical Moist Convection Using Machine Learning. AGU Fall Meeting, San Francisco, CA, December 2023 [invited]

Lamb, Kara D., et al. Learning Constraints on Depositional Ice Growth Models from Cloud Chamber Experiments with Physics Informed Neural Networks. AMS, 2024, https://ams.confex.com/ams/104ANNUAL/meetingapp.cgi/Paper/437022.

Lamb, Kara D., M. van Lier Walqui, S. Santos, H. Morrison. Reduced Order Modeling to Reduce Structural Uncertainty in Representing Cloud Microphysical Process Rates. AGU Fall Meeting 2023. San Francisco, CA, December 2023.

Lamb, Kara D. and P. Gentine. Zero shot learning of aerosol optical properties with graph neural networks. EGU General Assembly, April 2024. [invited]

Lamb, Kara, et al. A differentiable framework to reduce structural and parametric uncertainty in cloud microphysics parameterizations online. AGU, 2024. https://agu.confex.com/agu/agu24/meetingapp.cgi/Paper/1760552.

Lamb, Kara, et al. Discovering a Model for Depositional Ice Growth from Observations Using Neural Ordinary Differential Equations and Symbolic Regression. AGU, 2024. https://agu.confex.com/agu/agu24/meetingapp.cgi/Paper/1625362.

Lawrence, David. Introduction to the Community Earth System Model Version 3 (CESM3). AGU, 2025. https://agu.confex.com/agu/agu25/meetingapp.cgi/Session/249137.

Li, Shuolin, Zheng, Tian, and Gentine, Pierre. Probabilistic Distribution-Driven Data Assimilation Framework for Climatic and Hydrological Science. AGU, 2024. https://agu.confex.com/agu/agu24/meetingapp.cgi/Paper/1579133.

Liu, Shengjie, et al. The Opportunity Cost of 2023 Canadian Wildfires. AGU, 2025. https://agu.confex.com/agu/agu25/meetingapp.cgi/Paper/1974812.

Loftus, Kaitlyn, et al. Parameterizing Cloud Microphysics with Machine Learning-Enabled Bayesian Parameter Inference. AMS, 2024, https://ams.confex.com/ams/104ANNUAL/meetingapp.cgi/Paper/435624.

Loftus, Kaitlyn, et al. Parameterizing Cloud Microphysics with Machine Learning-Enabled Bayesian Parameter Inference. AGU, 2023, https://agu.confex.com/agu/fm23/meetingapp.cgi/Paper/1429616.

Lu, Isabella, et al. Investigating Snowpack-Shrub Interactions in the Arctic Tundra using Machine Learning and Process Models. AGU, 2025. https://agu.confex.com/agu/agu25/meetingapp.cgi/Paper/1898345.

Lynch, Brycen, et al. Forest Dynamics in South America Under a Changing Climate: An Analysis Using ModelE-BiomeE with ERA5. AGU, 2025. https://agu.confex.com/agu/agu25/meetingapp.cgi/Paper/1973649.

Matai, Racheet, et al. Deep Learning for identifying functional relationships involving basal friction and ice fluidity. AGU, 2024. https://agu.confex.com/agu/agu24/meetingapp.cgi/Paper/1728635.

McEvoy, Kyle, et al. Perturbed Parameter Ensembles and Precipitation Quantile Uncertainty. AGU, 2024. https://agu.confex.com/agu/agu24/meetingapp.cgi/Paper/1593581.

McKinley, et al. Constraining historical ocean carbon uptake with models, machine learning and data. World Climate Research Program Open Science Conference. Rwanda, Oct. 2023.

McKinley, Galen, et al. Ocean carbon sink uncertainties at the scale of mCDR. AGU, 2024. https://agu.confex.com/agu/agu24/meetingapp.cgi/Paper/1702200.

Mikhaeil, Jonas Magdy, et al. Bayesian Workflow for the Evaluation of Constraints on Depositional Ice Growth Models with Cloud Chamber Observations. AMS, 2024, https://ams.confex.com/ams/104ANNUAL/meetingapp.cgi/Paper/436623.

Nathaniel, Juan, et al. Deep Generative Data Assimilation in Multimodal Setting. AGU, 2024. https://agu.confex.com/agu/agu24/meetingapp.cgi/Paper/1522748.

Nayak, Adam, et al. A Nonstationary Stochastic Simulator for Clustered Regional Hydroclimatic Extremes to Characterize Compound Flood Risk. AGU, 2024. https://agu.confex.com/agu/agu24/meetingapp.cgi/Paper/1566951.

Nguyen, Ashley, et al. Emulating Lagrangian Ice Crystal Evolution in Cirrus Clouds Using Supervised Machine Learning. AGU, 2024. https://agu.confex.com/agu/agu24/meetingapp.cgi/Paper/1721206.

Nicolaou, Giorgia, et al. Inferring Thermodynamic Histories from In Situ Ice Crystal Imagery via Conditional Diffusion Models. AGU, 2025. https://agu.confex.com/agu/agu25/meetingapp.cgi/Paper/1970533.

Pizmony-Levy, Oren, Rivet, Ann, Torres, Chris, and Urbach, Noa. New York City Educators’ Perceptions of Students’ Engagement with Climate Change. Annual Meeting of the Comparative and International Education Society (CIES). Miami, Florida (2024). Presenter: Urbach, Noa

Pizmony-Levy, Oren, Rivet, Ann, Torres, Chris, and Urbach, Noa. Tik Tok, Cheese Sticks, and Our Future: How Educators Perceive Students’ Engagement with Climate Change. Annual Meeting of the American Educational Research Association (AERA). Philadelphia, Pennsylvania (2024). Presenters: Torres, Chris, and Urbach, Noa

Qu, Yongquan, and Gentine, Pierre. Joint Parameter and Parameterization Inference with Uncertainty Quantification through Differentiable Programming. AGU, 2024. https://agu.confex.com/agu/agu24/meetingapp.cgi/Paper/1594937.

Rezaali, Mostafa, et al. Spatial Clustering of heat wave Regimes Through ConvAE Latent Space Analysis and Teleconnection Linkages. AGU, 2025. https://agu.confex.com/agu/agu25/meetingapp.cgi/Paper/1856911.

Rozanov, Aleksei, et al. DeepCarbon: A Global Data Product of Upscaled Carbon Fluxes from Knowledge-Guided Deep Learning Models. AGU, 2025. https://agu.confex.com/agu/agu25/meetingapp.cgi/Paper/1912682.

Ryu, Sarah, et al. Diagnosing Parametric Controls on CLM Ecohydrology Using Machine Learning Emulators. AGU, 2025. https://agu.confex.com/agu/agu25/meetingapp.cgi/Paper/1985787.

Sampath, Akila, et al. Deep Contrastive Learning for Microphysics Scheme Comparison in a Perturbed Initial Condition CESM Ensemble. AGU, 2025. https://agu.confex.com/agu/agu25/meetingapp.cgi/Paper/2001023.

Semie, Addisu, et al. Improving Extreme Precipitation Representation with ML Based Warm Microphysics in CESM. AGU, 2025. https://agu.confex.com/agu/agu25/meetingapp.cgi/Paper/1906769.

Stephens, Troy, et al. Inferring Instantaneous Depositional Ice Growth Rates From Ice Crystal Images. AGU, 2025. https://agu.confex.com/agu/agu25/meetingapp.cgi/Paper/1985176.

van Lier-Walqui, Marcus, and Elsaesser, Gregory S. A Calibrated Physics Ensemble (CPE) to Represent Climate Model Uncertainty and Guide Development of Observing Systems. AMS, 2025. https://ui.adsabs.harvard.edu/abs/2025AMS…10557705V/abstract.

van Lier-Walqui, Marcus, et al. Challenges and opportunities for unified top-down and bottom-up constraint of ESM parameterizations. AGU, 2024. https://agu.confex.com/agu/agu24/meetingapp.cgi/Paper/1720912.

Watson-Parris, Duncan. From Regulation to Radiative Forcing: Constraining the Climate Response to Declining Shipping Aerosol Emissions. AGU, 2025. (invited) https://agu.confex.com/agu/agu25/meetingapp.cgi/Paper/1857693.

Watson-Parris, Duncan, et al. JCM: A Differentiable Testbed for Hybrid Atmospheric Modeling. AGU, 2025. https://agu.confex.com/agu/agu25/meetingapp.cgi/Paper/1895655.

Watson-Parris, Duncan, et al. ClimateBench2.0: Probabilistic Climate Model Scoring. AGU, 2025. https://agu.confex.com/agu/agu25/meetingapp.cgi/Paper/1922122.

Yang, Qingyuan, et al. Flexible Use of Additive Gaussian Processes as a Powerful Tool for More Interpretable Analysis and Emulation of Climate Model PPEs. AGU, 2023, https://agu.confex.com/agu/fm23/meetingapp.cgi/Paper/1341071.

Yang, Qingyuan, and Susanna Jenkins. Two Sources of Uncertainty in Estimating Tephra Volumes from Isopachs: Perspectives and Quantification. AGU, 2023, https://agu.confex.com/agu/fm23/meetingapp.cgi/Paper/1343724.

Yang, Qingyuan, et al. Structural error-aware climate model calibration with global, local and zonal climatologies using Perturbed Parameter Ensembles (PPEs). AGU, 2025. https://agu.confex.com/agu/agu25/meetingapp.cgi/Paper/1973654.

Yang, Qingyuan, et al. Insights from emulating CAM6 PPE with Neural Network and an additive emulator. AGU, 2024. https://agu.confex.com/agu/agu24/meetingapp.cgi/Paper/1723358.

Zanna, Laure. The New Generation of Global Climate Models Enhanced by Machine Learning (Invited). AGU 2023. https://agu.confex.com/agu/fm23/meetingapp.cgi/Paper/1366219.