YEAR 2 || ANNUAL MEETING
Cited Papers
Climate Model Tuning
- Hourdin, F., Mauritsen, T., Gettelman, A., Golaz, J., Balaji, V., Duan, Q., Folini, D., Ji, D., Klocke, D., Qian, Y., Rauser, F., Rio, C., Tomassini, L., Watanabe, M., and Williamson, D. (2017). The Art and Science of Climate Model Tuning. Bulletin of the American Meteorological Society 98(3), 589-602. doi:10.1175/BAMS-D-15-00135.1
Mauritsen, T., et al. (2012), Tuning the climate of a global model, J. Adv. Model. Earth Syst., 4, M00A01. doi:10.1029/2012MS000154.
Cryosphere
- Arthern, R. J., R. C. A. Hindmarsh, and C. R. Williams (2015), Flow speed within the Antarctic ice sheet and its controls inferred from satellite observations, J. Geophys. Res. Earth Surf., 120, 1171 – 1188. doi:10.1002/2014JF003239.
- , , , , , & (2022). The impact of basal roughness on inland Thwaites Glacier sliding. Geophysical Research Letters, 49, e2021GL096564. https://doi.org/10.1029/2021GL096564
- Minchew, Brent & Joughin, Ian. (2020). Toward a universal glacier slip law. Science. 368. 29-30. 10.1126/science.abb3566.
- Schoof, C. (2007), Ice sheet grounding line dynamics: Steady states, stability, and hysteresis, J. Geophys. Res., 112, F03S28, doi:10.1029/2006JF000664.
- Zoet, Lucas & Iverson, Neal. (2020). A slip law for glaciers on deformable beds. Science. 368. 76-78. 10.1126/science.aaz1183.
DEI
- Ali, H.N., Sheffield, S.L., Bauer, J.E. et al. An actionable anti-racism plan for geoscience organizations. Nat Commun 12, 3794 (2021). https://doi.org/10.1038/s41467-021-23936-w
Handling Uncertainty
- Barnes, E.A., Barnes, R.J., & Gordillo, N. (2021). Adding Uncertainty to Neural Network Regression Tasks in the Geosciences. https://arxiv.org/abs/2109.07250
Low Cloud Turbulence
- Chow, F.K.; Schär, C.; Ban, N.; Lundquist, K.A.; Schlemmer, L.; Shi, X. Crossing Multiple Gray Zones in the Transition from Mesoscale to Microscale Simulation over Complex Terrain. Atmosphere 2019, 10, 274. https://doi.org/10.3390/atmos10050274
- , , , , , , et al. (2020). The atmospheric boundary layer and the “gray zone” of turbulence: A critical review. Journal of Geophysical Research: Atmospheres, 125, e2019JD030317. https://doi.org/10.1029/2019JD030317
- Li, Z., Kovachki, N.B., Azizzadenesheli, K., Liu, B., Bhattacharya, K., Stuart, A., & Anandkumar, A. (2020). Fourier Neural Operator for Parametric Partial Differential Equations. ArXiv, abs/2010.08895.
- Ramadhan, A., Marshall, J., Souza, A., Wagner, G. L., Ponnapati, M., & Rackauckas, C. (2020). Capturing missing physics in climate model parameterizations using neural differential equations. arXiv preprint arXiv:2010.12559.
Metrics
- , , , & (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, e2021MS002620. https://doi.org/10.1029/2021MS002620
Parameter Inference
- Bhouri, M. A., & Gentine, P. (2022). History-Based, Bayesian, Closure for Stochastic Parameterization: Application to Lorenz’96. arXiv preprint arXiv:2210.14488.
- 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.
