Williamson, Daniel

The Art and Science of Climate Model Tuning

Hourdin, F., Mauritsen, T., Gettelman, A., Golaz, J.-C., Balaji, V., Duan, Q., Folini, D., Ji, D., Klocke, D., Qian, Y., et al. (2017) · Bulletin of the American Meteorological Society

Climate variability and modelsMeteorological Phenomena and Simulations

Cited by 611

Process‐Based Climate Model Development Harnessing Machine Learning: I. A Calibration Tool for Parameterization Improvement

Couvreux, F., Hourdin, F., Williamson, D., Roehrig, R., Volodina, V., Villefranque, N., Rio, C., Audouin, O., Salter, J., Bazile, E., et al. (2021) · Journal of Advances in Modeling Earth Systems

Meteorological Phenomena and SimulationsClimate variability and models

Cited by 121

Process‐Based Climate Model Development Harnessing Machine Learning: II. Model Calibration From Single Column to Global

Hourdin, F., Williamson, D., Rio, C., Couvreux, F., Roehrig, R., Villefranque, N., Musat, I., Fairhead, L., Diallo, F. B., & Volodina, V. (2021) · Journal of Advances in Modeling Earth Systems

Meteorological Phenomena and SimulationsClimate variability and models

Cited by 83

Tuning without over-tuning: parametric uncertainty quantification for the NEMO ocean model

Williamson, D. B., Blaker, A. T., & Sinha, B. (2017) · Geoscientific Model Development

Oceanographic and Atmospheric ProcessesClimate variability and models

Cited by 75

Toward machine-assisted tuning avoiding the underestimation of uncertainty in climate change projections

Hourdin, F., Ferster, B., Deshayes, J., Mignot, J., Musat, I., & Williamson, D. (2023) · Science Advances

Climate variability and modelsMeteorological Phenomena and Simulations

Cited by 49