Publications, Papers, Conferences

Center Publications

Peer Reviewed Publications

  • Bennington, V., Galjanic, T., & McKinley, G. A. (2022). Estimating historical air-sea CO2 fluxes: Incorporating physical knowledge within a data-only approach. GRL in review, https://www.essoar.org/doi/abs/10.1002/essoar.10510815.1
  • Bennington, V., Gloege, L., & McKinley, G. A. (2022). Observation-based variability in the global ocean carbon sink from 1959-2020. JAMES, in review. https://www.essoar.org/doi/abs/10.1002/essoar.10510196.1
  • Cheng, Y., Giometto, M. G., Kauffmann, P., Lin, L., Cao, C., Zupnick, C., et al. (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.
  • Tran, H., Leonarduzzi, E., De la Fuente, L., Hull, R. B., Bansal, V., Chennault, C., … & Maxwell, R. M. (2021). Development of a Deep Learning Emulator for a Distributed Groundwater–Surface Water Model: ParFlow-ML. Water, 13(23), 3393.
  • 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

  • Beucler, T., Pritchard, M., Yuval, J., Gupta, A., Peng, L., Rasp, S., … & Gentine, P. (2021). Climate-Invariant Machine Learning. arXiv preprint 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 preprint arXiv:2112.11317.

Synergistic Publications

Peer Reviewed Publications
  • Abraham, M., & Burbano, V. (2022). 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.
  • 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.
  • 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.
  • Loose, N., Abernathey, R., Grooms, I., Busecke, J., Guillaumin, A., Yankovsky, E., … & Martin, P. (2022). GCM-Filters: A Python Package for Diffusion-based Spatial Filtering of Gridded Data. Journal of Open Source Software, 7(70), 3947.
  • 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. (2020). Integrating scientific knowledge with machine learning for engineering and environmental systems. arXiv preprint arXiv:2003.04919.

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., Khandelwal, A., Jia, X., Li, X., Neiber, J., … & Kumar, V. (2021). Knowledge-guided Self-supervised Learning for estimating River-Basin Characteristics. arXiv preprint arXiv:2109.06429.
  • Massmann, A., Gentine, P., & Runge, J. (2021). Causal inference for process understanding in Earth sciences. arXiv preprint arXiv:2105.00912.

Conference Presentations and Proceedings

  • 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.
  • 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 preprint arXiv:2009.12575.
  • McKinley, G.A. (2022, May). Constraining Models of the Future Carbon Sink with Observations and Machine learning. In GRC Ocean Biogeochemistry Conference 2022. GRC.
  • Pincus, R. (2021, November). Atmospheric radiation: using machine learning for the unknowable and uncomputable. In KITP Conference: Machine Learning for Climate 2021. KITP.
  • 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.
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