Publications, Papers + Conferences

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


Non-Peer Reviewed Publications

Peer Reviewed Publications


Non-Peer Reviewed Publications

Peer Reviewed Publications


Non-Peer
Reviewed Publications

  • 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.
  • Buch, J., Williams, A. P., Juang, C. S., Hansen, W. D., & Gentine, P. SMLFire1. 0: a stochastic machine learning (SML) model for wildfire activity in the western United States. EGUsphere, 1-39, Nov. 2022, https://doi.org/10.5194/egusphere-2022-1148.

Peer Reviewed Publications


Non-Peer Reviewed Publications

Synergistic Publications

Peer Reviewed Publications

  • Falasca, Fabrizio et al. “Data-driven dimensionality reduction and causal inference for spatiotemporal climate fields” Physical Review E 109.4 (2024): 044202. doi.org/10.1103/PhysRevE.109.044202.
  • 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.
  • 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.


Non-Peer Reviewed Publications

  • 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.
  • 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.

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, et al. “The Gender Gap in Meaningful Work.” Management Science, Articles in Advance, Dec. 2023, pp.1-20, 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, et al. “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.
  • 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 Data Assimilation Increments.” Journal of Advances in Modeling Earth Systems, vol. 15, no. 10, 2023, p. e2023MS003757, https://doi.org/10.1029/2023MS003757.
  • 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.
  • Lamb, Kara and Pierre Gentine. Zero-shot learning of Aerosol Optical Properties with Graph Neural Networks. Scientific Reports, 13, 18777 (2023). https://www.nature.com/articles/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.
  • 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 Vertical Buoyancy Fluxes in the Ocean Mixed Layer. arXiv:2312.06972, arXiv, 11 Dec. 2023, https://doi.org/10.48550/arXiv.2312.06972.
  • Burbano, Vanessa, Nicolas Padilla and Stephan Meier. “Gender Differences in Preferences for Meaning at Work.” August 26, 2023.
  • 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.
  • 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.

Peer Reviewed Publications


Non-Peer Reviewed Publications

Peer Reviewed Publications

 

Books and Book Chapters

 

Non-Peer Reviewed Publications

Peer Reviewed Publications

Books + Book Chapters

  • 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

  • Acquaviva, Viviana, “From ML x Astrophysics to ML x Climate: A journey across disciplines”, PIVOT fellowship symposium, April 2024.
  • Acquaviva, Viviana, Elizabeth Barnes, David John Gagne II, Galen McKinley, Savannah Thais, “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/.
  • 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.
  • 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.
  • 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 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.
  • 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.
  • 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.
  • Pizmony-Levy, Oren, Ann Rivet, Christina Torres, and Noa Urbach. 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, Ann Rivet, Christina Torres, and Noa Urbach. 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.