Source Themes

A new conceptual model of global ocean heat uptake

J.M. Gregory, J Bloch-Johnson, M.P. Couldrey, E Exarchou, S.M. Griffies, T Kuhlbrodt, Emily Newsom, O.A. Saenko, T. Suzuki, Q. Wu, S. Urakawa, Laure Zanna

A Data-Driven Approach for Parameterizing Submesoscale Vertical Buoyancy Fluxes in the Ocean Mixed Layer

Abigail Bodner, D. Balwada, Laure Zanna

Discovering causal relations and equations from data

G. Camps-Valls, A. Gerhardus, U. Nimad, G. Varando, G. Martius, E. Balaguer-Ballester, R. Vinuesa, E. Diaz, Laure Zanna, J. Runge

Background Pycnocline depth constrains Future Ocean Heat Uptake Efficiency

Emily Newsom, Laure Zanna, J. M. Gregory

Implementation of a data-driven equation-discovery mesoscale parameterization into an ocean model

Pavel Perezhogin, C. Zhang, A. Adcroft, C. Fernandez-Granda, Laure Zanna

Remote Versus Local Impacts of Energy Backscatter on the North Atlantic SST Biases in a Global Ocean Model

C-Y Chang, A. Adcroft, Laure Zanna, R. Hallberg, S.M. Griffies

Parameterizing Vertical Mixing Coefficients in the Ocean Surface Boundary Layer using Neural Networks

A. Sane, B. G. Reichl, A. Adcroft, Laure Zanna

Generative data-driven approaches for stochastic subgrid parameterizations in an idealized ocean model

Pavel Perezhogin, C. Fernandez-Granda, Laure Zanna

Implementation and Evaluation of a Machine Learned Mesoscale Eddy Parameterization into a Numerical Ocean Circulation Model

C. Zhang, Pavel Perezhogin, C. Gultekin, A. Adcroft, C. Fernandez-Granda, Laure Zanna

Deep learning of systematic sea ice model errors from data assimilation increments

W. Gregory, M. Bushuk, A. Adcroft, Y. Zhang, Laure Zanna