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
- ElGhawi, R., Kraft, B., Reimers, C., Reichstein, M., Körner, M., Gentine, P., & WinklerWinkler, A. J. (2023). Hybrid modeling of evapotranspiration: inferring stomatal and aerodynamic resistances using combined physics-based and machine learning. Environmental Research Letters, 18(3), 034039
- Fay, A. R., McKinley, G. A., Lovenduski, N. S., Eddebbar, Y., Levy, M. N., Long, M. C., … & Rustagi, R. R. (2023). Immediate and Long‐Lasting Impacts of the Mt. Pinatubo Eruption on Ocean Oxygen and Carbon Inventories. Global Biogeochemical Cycles, 37(2), e2022GB007513
- Gruber, N., Bakker, D. C., DeVries, T., Gregor, L., Hauck, J., … McKinley, G.A., Müller, J. D. (2023). Trends and variability in the ocean carbon sink. Nature Reviews Earth & Environment, 4(2), 119-134
- Grundner, A., Beucler, T., Gentine, P., & Eyring, V. (2023). Data-Driven Equation Discovery of a Cloud Cover Parameterization. arXiv preprint arXiv:2304.08063
- Iglesias-Suarez, F., Gentine, P., Solino-Fernandez, B., Beucler, T., Pritchard, M., Runge, J., & Eyring, V. (2023). Causally-informed deep learning to improve climate models and projections. arXiv preprint arXiv:2304.12952
- Kaps, A., Lauer, A., Camps-Valls, G., Gentine, P., Gómez-Chova, L., & Eyring, V. (2023). Machine-learned cloud classes from satellite data for process-oriented climate model evaluation. IEEE Transactions on Geoscience and Remote Sensing
- McKinley, G. A., Bennington, V., Meinshausen, M., & Nicholls, Z. (2023). Modern air-sea flux distributions reduce uncertainty in the future ocean carbon sink. Environmental Research Letters, 18(4), 044011
- Shen, C., Appling, A. P., Gentine, P., Bandai, T., Gupta, H., Tartakovsky, A., … & Lawson, K. (2023). Differentiable modeling to unify machine learning and physical models and advance Geosciences. arXiv preprint arXiv:2301.04027
- Skulovich, O., & Gentine, P. (2023). A Long-term Consistent Artificial Intelligence and Remote Sensing-based Soil Moisture Dataset. Scientific Data, 10(1), 154
Peer Reviewed Publications
- Behrens, G., Beucler, T., Gentine, P., Iglesias‐Suarez, F., Pritchard, M., & Eyring, V. (2022). Non‐Linear Dimensionality Reduction With a Variational Encoder Decoder to Understand Convective Processes in Climate Models. Journal of advances in modeling earth systems, 14(8), e2022MS003130
- Bennington, V., Galjanic, T., & McKinley, G. A. (2022). Explicit Physical Knowledge in Machine Learning for Ocean Carbon Flux Reconstruction: The pCO2‐Residual Method. Journal of Advances in Modeling Earth Systems, 14(10), e2021MS002960
- Bennington, V., Gloege, L., & McKinley, G. A. (2022). Variability in the global ocean carbon sink from 1959 to 2020 by correcting models with observations. Geophysical Research Letters, 49(14), e2022GL098632
- Buch, J., Williams, A. P., Juang, C. S., Hansen, W. D., & Gentine, P. (2022). SMLFire1. 0: a stochastic machine learning (SML) model for wildfire activity in the western United States. EGUsphere, 1-39
- Chen, T. C., Penny, S. G., Whitaker, J. S., Frolov, S., Pincus, R., & Tulich, S. (2022). Correcting Systematic and State‐Dependent Errors in the NOAA FV3‐GFS Using Neural Networks. Journal of Advances in Modeling Earth Systems, 14(11).
- Cheng, Y., Giometto, M. G., Kauffmann, P., Lin, L., Cao, C., Zupnick, C., Abernathey, R. & Gentine, P. (2022). 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.
- Mooers, G., Beucler, T., Pritchard, M., & Mandt, S. (2022). An Unsupervised Learning Perspective on the Dynamic Contribution to Extreme Precipitation Changes. arXiv preprint arXiv:2211.01613.
- Mooers, G., Pritchard, M., Beucler, T., Srivastava, P., Mangipudi, H., Peng, L., … & Mandt, S. (2022). Comparing Storm Resolving Models and Climates via Unsupervised Machine Learning. arXiv preprint arXiv:2208.11843
- Shamekh, S., Lamb, K. D., Huang, Y., & Gentine, P. (2022). Implicit learning of convective organization explains precipitation stochasticity. Authorea Preprints
- Wong, S. C., McKinley, G. A., & Seager, R. (2022). Equatorial Pacific pCO2 interannual variability in CMIP6 models. Journal of Geophysical Research: Biogeosciences, e2022JG007243
- Zhan, W., Yang, X., Ryu, Y., Dechant, B., Huang, Y., Goulas, Y., … & Gentine, P. (2022). 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.
Non-Peer Reviewed Publications
- Buch, J., Williams, A. P., Juang, C. S., Hansen, W. D., & Gentine, P. (2022). Modeling wildfire activity in the western United States with machine learning. Authorea Preprints
Peer Reviewed Publications
- Tran, H., Leonarduzzi, E., De la Fuente, L., Hull, R. B., Bansal, V., Chennault, C., Gentine, P. … & Maxwell, R. M. (2021). Development of a Deep Learning Emulator for a Distributed Groundwater–Surface Water Model: ParFlow-ML. Water, 13(23), 3393.
- Tran, H., Leonarduzzi, E., De la Fuente, L., Hull, R. B., Bansal, V., Chennault, C., Gentine, P. … & Maxwell, R. M. (2021). Development of a Deep Learning Emulator for a Distributed Groundwater–Surface Water Model: ParFlow-ML. Water, 13(23), 3393.
Non-Peer Reviewed Publications
- Beucler, T., Pritchard, M., Yuval, J., Gupta, A., Peng, L., Rasp, S., … & Gentine, P. (2021). Climate-Invariant Machine Learning. arXiv:2112.08440.
- Grundner, A., Beucler, T., Iglesias-Suarez, F., Gentine, P., Giorgetta, M. A., & Eyring, V. (2021). Deep Learning Based Cloud Cover Parameterization for ICON. arXiv:2112.11317
- Massman, A., Gentine, P. & Runge, J. Causal inference for process understanding in Earth sciences. arXiv:2105.00912.
Synergistic Publications
Peer Reviewed Publications
- Broderick, T., Gelman, A., Meager, R., Smith, A. L., & Zheng, T. (2021). Toward a Taxonomy of Trust for Probabilistic Machine Learning. arXiv preprint arXiv:2112.03270
- Christensen, H., & Zanna, L. (2022). Parameterization in Weather and Climate Models. In Oxford Research Encyclopedia of Climate Science
- Gloege, L., Yan, M., Zheng, T., & McKinley, G. A. (2022). Improved quantification of ocean carbon uptake by using machine learning to merge global models and pCO2 data. Journal of Advances in Modeling Earth Systems, 14(2), e2021MS002620.
- Loose, N., Abernathey, R., Grooms, I., Busecke, J., Guillaumin, A., Yankovsky, E., … Zanna, L. & Martin, P. (2022). GCM-Filters: A Python Package for Diffusion-based Spatial Filtering of Gridded Data. Journal of Open Source Software, 7(70), 3947.
- Ross, A., Li, Z., Perezhogin, P., Fernandez‐Granda, C., & Zanna, L. (2023). Benchmarking of machine learning ocean subgrid parameterizations in an idealized model. Journal of Advances in Modeling Earth Systems, 15(1), e2022MS003258
- Russotto, R. D., Strong, J. D., Camargo, S. J., Sobel, A., Elsaesser, G. S., Kelley, M., … & Kim, D. (2022). Evolution of Tropical Cyclone Properties Across the Development Cycle of the GISS E3 Global Climate Model. Journal of Advances in Modeling Earth Systems, 14(1), e2021MS002601.
- Wang, R., Li, L., Gentine, P., Zhang, Y., Chen, J., Chen, X., … & Lü, G. (2022). Recent increase in the observation-derived land evapotranspiration due to global warming. Environmental Research Letters, 17(2), 024020.
- Willard, J., Jia X., Xu, S., Steinbach, M. & Kumar, V. (2022). Integrating Scientific Knowledge with Machine Learning for Engineering and Environmental Systems. arXiv:2003.04919.
Non-Peer Reviewed Publications
- Portocarrero, F., & Burbano, V. (2022). Doing Well by Requiring Employees to Do Good: Field Experimental Evidence of the Effects of a One-time, Mandatory Corporate Social Intervention on Employees. Mandatory Corporate Social Intervention on Employees (May 25, 2022)
Peer Reviewed Publications
- Burbano, V.C. & Abraham, M. (2021). Congruence Between Leadership Gender and Organizational Claims Affects the Gender Composition of the Applicant Pool: Field Experimental Evidence. Organization Science, 33(1):393-413.
- Burbano, V. C., & Chiles, B. (2021). Mitigating gig and remote worker misconduct: Evidence from a real effort experiment. Organization Science.
- Crisp, D., Dolman, H., Tanhua, T., McKinley, G. A., Hauck, J., Eggleston, S., & Aich, V. (2021). How Well Do We Understand the Land-Ocean-Atmosphere Carbon Cycle?. Earth and Space Science Open Archive ESSOAr.
- Fay, A. R., Gregor, L., Landschützer, P., McKinley, G. A., Gruber, N., Gehlen, M., … & Zeng, J. (2021). SeaFlux: harmonization of air–sea CO2 fluxes from surface pCO2 data products using a standardized approach. Earth System Science Data, 13(10), 4693-4710.
- Fay, A. R., & McKinley, G. A. (2021). Observed regional fluxes to constrain modeled estimates of the ocean carbon sink. Geophysical Research Letters, 48(20), e2021GL095325.
- Gloege, L., McKinley, G. A., Landschützer, P., Fay, A. R., Frölicher, T. L., Fyfe, J. C., et al. (2021). Quantifying errors in observationally based estimates of ocean carbon sink variability. Global Biogeochemical Cycles, 35, e2020GB006788.
- Jia, X., Willard, J., Karpatne, A., Read, J. S., Zwart, J. A., Steinbach, M., & Kumar, V. (2021). Physics-guided machine learning for scientific discovery: An application in simulating lake temperature profiles. ACM/IMS Transactions on Data Science, 2(3), 1-26.
Books and Book Chapters
- Gentine, P., Eyring, V., & Beucler, T. (2021). Deep Learning for the Parametrization of Subgrid Processes in Climate Models. Deep Learning for the Earth Sciences: A Comprehensive Approach to Remote Sensing, Climate Science, and Geosciences, 307-314.
- Zanna, L., & Bolton, T. (2021). Deep Learning of Unresolved Turbulent Ocean Processes in Climate Models. Deep Learning for the Earth Sciences: A Comprehensive Approach to Remote Sensing, Climate Science, and Geosciences, 298-306.
Non-Peer Reviewed Publications
- Ghosh, R., Renganathan, A., Tayal, K., Li, X., Khandelwal, A., Jia, X., Duffy, C., … & Kumar, V. (2021). Robust Inverse Framework using Knowledge-guided Self-supervised Learning: An application to Hydrology. arXiv:2109.06429.
- Massmann, A., Gentine, P., & Runge, J. (2021). Causal inference for process understanding in Earth sciences. arXiv:2105.00912.
Peer Reviewed Publications
- Willard, J., Jia, X., Xu, S., Steinbach, M., & Kumar, V. (2020). Integrating scientific knowledge with machine learning for engineering and environmental systems. arXiv:2003.04919.
Conference Presentations and Proceedings
- McKinley, G.A. (2022). Constraining Models of the Future Carbon Sink with Observations and Machine learning. In GRC Ocean Biogeochemistry Conference 2022. GRC.
- Salas-Porras, E. D., Tazi, K., Braude, A., Okoh, D., Lamb, K. D., Watson-Parris, D., … & Meinert, N. (2022). Identifying the Causes of Pyrocumulonimbus (PyroCb). arXiv preprint arXiv:2211.08883
- Tang, C., Lenssen, N., Wei, Y., & Zheng, T. (2022). Wasserstein Distributional Learning. arXiv preprint arXiv:2209.04991
- Tazi, K., Salas-Porras, E. D., Braude, A., Okoh, D., Lamb, K. D., Watson-Parris, D., … & Meinert, N. (2022). Pyrocast: a machine learning pipeline to forecast pyrocumulonimbus (pyrocb) clouds. arXiv preprint arXiv:2211.13052
- Tayal, K., Jia, X., Ghosh, R., Willard, J., Read, J., & Kumar, V. (2022). Invertibility aware Integration of Static and Time-series data: An application to Lake Temperature Modeling. In Proceedings of the 2022 SIAM International Conference on Data Mining (SDM) (pp. 702-710). Society for Industrial and Applied Mathematics.
- van Lier-Walqui, M., Elsaesser, G., Gettelman, A., Fridlind, A., Ackerman, A., … & Santos, S. (2022). Use of single-column models to constrain and investigate climate model physical processes and comparison with global tuning. In 3rd Pan-GASS Meeting Understanding and Modeling Atmospheric Processes 2022. GASS.
- Elsaesser, G., van Lier-Walqui, M., Wu, J., Singh, J., & Una, R. (2021, December). The Impact of Observational Errors on the Optimization of GCM Free Parameters. In AGU Fall Meeting 2021. AGU.
- Pincus, R. (2021, November). Atmospheric radiation: using machine learning for the unknowable and uncomputable. In KITP Conference: Machine Learning for Climate 2021. KITP.
- Jia, X., Zwart, J., Sadler, J., Appling, A., Oliver, S., Markstrom, S., … & Kumar, V. (2020). Physics-guided recurrent graph networks for predicting flow and temperature in river networks. arXiv:2009.12575.
