Publications

USMILE publications

Peer-Reviewed Publications 2024

  • Karmouche, S., Galytska, E., Meehl, G. A., Runge, J., Weigel, K., and Eyring, V.: Changing effects of external forcing on Atlantic–Pacific interactions, Earth Syst. Dynam., 15, 689–715, https://doi.org/10.5194/esd-15-689-2024, 2024.
  • Eyring, V., P. Gentine, G. Camps-Valls, D. M. Lawrence, and M. Reichstein, AI-empowered next-generation multiscale climate modelling for mitigation and adaptation, Nat. Geosci., https://doi.org/10.1038/s41561-024-01527-w, 2024. Available at https://rdcu.be/dU3IS.
  • Eyring, V., et al., Pushing the frontiers in climate modelling and analysis with machine learning, Nature Climate Change, https://doi.org/10.1038/s41558-024-02095-y, 2024. Available at https://rdcu.be/dRMdh.
  • Heuer, H., Schwabe, M., Gentine, P., Giorgetta, M. A., & Eyring, V.,  Interpretable multiscale machine learning‐based parameterizations of convection for ICON, Journal of Advances in Modeling Earth Systems, 16(8), e2024MS004398, https://doi.org/10.1029/2024MS004398, 2024.
  • Debeire, K., Gerhardus, A., Runge, J., Eyring, V., Bootstrap aggregation and confidence measures to improve time series causal discovery, Proceedings of the Third Conference on Causal Learning and Reasoning, 236:979-1007, 2024. Available from https://proceedings.mlr.press/v236/debeire24a.html.
  • Gier, B. K., Schlund, M., Friedlingstein, P., Jones, C. D., Jones, C., Zaehle, S., and Eyring, V.: Representation of the terrestrial carbon cycle in CMIP6. Biogeosciences, 21, 5321–5360, https://doi.org/10.5194/bg-21-5321-2024, 2024.
  • Grundner, A., T. Beucler, P. Gentine, V. Eyring, Data-Driven Equation Discovery of a Cloud Cover Parameterization, Journal of Advances in Modeling Earth Systems (JAMES), https://doi.org/10.1029/2023MS003763, 2024.
  • Iglesias-Suarez, F., Gentine, P., Solino-Fernandez, B., Beucler, T., Pritchard, M., Runge, J., and Eyring, V.: Causally-informed deep learning to improve climate models and projections. Journal of Geophysical Research: Atmospheres, 129, e2023JD039202, https://doi.org/10.1029/2023JD039202, 2024.
  • Jones, C. G., Adloff, F., Booth, B. B. B., Cox, P. M., Eyring, V., Friedlingstein, P., Frieler, K., Hewitt, H. T., Jeffery, H. A., Joussaume, S., Koenigk, T., Lawrence, B. N., O’Rourke, E., Roberts, M. J., Sanderson, B. M., Séférian, R., Somot, S., Vidale, P. L., van Vuuren, D., Acosta, M., Bentsen, M., Bernardello, R., Betts, R., Blockley, E., Boé, J., Bracegirdle, T., Braconnot, P., Brovkin, V., Buontempo, C., Doblas-Reyes, F., Donat, M., Epicoco, I., Falloon, P., Fiore, S., Frölicher, T., Fučkar, N. S., Gidden, M. J., Goessling, H. F., Graversen, R. G., Gualdi, S., Gutiérrez, J. M., Ilyina, T., Jacob, D., Jones, C. D., Juckes, M., Kendon, E., Kjellström, E., Knutti, R., Lowe, J., Mizielinski, M., Nassisi, P., Obersteiner, M., Regnier, P., Roehrig, R., Salas y Mélia, D., Schleussner, C.-F., Schulz, M., Scoccimarro, E., Terray, L., Thiemann, H., Wood, R. A., Yang, S., and Zaehle, S.: Bringing it all together: science priorities for improved understanding of Earth system change and to support international climate policy, Earth Syst. Dynam., 15, 1319–1351, https://doi.org/10.5194/esd-15-1319-2024, 2024.​
  • Kaps, A., Lauer, A., Kazeroni, R., Stengel, M., and Eyring, V.: Characterizing clouds with the CCClim dataset, a machine learning cloud class climatology, Earth Syst. Sci. Data, 16, 3001-3016, https://doi.org/10.5194/essd-16-3001-2024, 2024.
  • Sarauer, E., Schwabe, M., Lauer, A., Stier, P., Weiss, P. and Eyring, V., Physics-informed Machine Learning-Based Cloud Microphysics Parameterization for Earth System Models. The Twelfth International Conference on Learning Representations. Workshop: Tackling Climate Change with Machine Learning, 35. https://climatechange.ai/papers/iclr2024/35, 2024.
  • Sungduk Yu, Zeyuan Hu, Akshay Subramaniam, Walter Hannah, Liran Peng, Jerry Lin, Mohamed Aziz Bhouri, Ritwik Gupta, Björn Lütjens, Justus C. Will, Gunnar Behrens, Julius J. M. Busecke, Nora Loose, Charles I. Stern, Tom Beucler, Bryce Harrop, Helge Heuer, Benjamin R. Hillman, Andrea Jenney, Nana Liu, Alistair White, Tian Zheng, Zhiming Kuang, Fiaz Ahmed, Elizabeth Barnes, Noah D. Brenowitz, Christopher Bretherton, Veronika Eyring, Savannah Ferretti, Nicholas Lutsko, Pierre Gentine, Stephan Mandt, J. David Neelin, Rose Yu, Laure Zanna, Nathan Urban, Janni Yuval, Ryan Abernathey, Pierre Baldi, Wayne Chuang, Yu Huang, Fernando Iglesias-Suarez, Sanket Jantre, Po-Lun Ma, Sara Shamekh, Guang Zhang, Michael Pritchard: ClimSim-Online: A Large Multi-scale Dataset and Framework for Hybrid ML-physics Climate Emulation. In 38th Conference on Neural Information Processing Systems, NeurIPS 2024. Advances in Neural Information Processing Systems. https://arxiv.org/abs/2306.08754, 2024.
  • Sanderson, B. M., Booth, B. B. B., Dunne, J., Eyring, V., Fisher, R. A., Friedlingstein, P., Gidden, M. J., Hajima, T., Jones, C. D., Jones, C. G., King, A., Koven, C. D., Lawrence, D. M., Lowe, J., Mengis, N., Peters, G. P., Rogelj, J., Smith, C., Snyder, A. C., Simpson, I. R., Swann, A. L. S., Tebaldi, C., Ilyina, T., Schleussner, C.-F., Séférian, R., Samset, B. H., van Vuuren, D., and Zaehle, S.: The need for carbon-emissions-driven climate projections in CMIP7, Geosci. Model Dev., 17, 8141–8172, https://doi.org/10.5194/gmd-17-8141-2024, 2024.

Peer-Reviewed Publications 2023

  • Paçal, A., Hassler, B., Weigel, K., Kurnaz, M. L., Wehner, M. F., & Eyring, V. Detecting extreme temperature events using Gaussian mixture models. Journal of Geophysical Research: Atmospheres. 128, e2023JD038906. https://doi.org/10.1029/2023JD038906, 2023.
  • ElGhawi, R., Kraft, B., Reimers, C., Reichstein, M., Körner, M., Gentine, P., & Winkler, A. J. Hybrid modeling of evapotranspiration: inferring stomatal and aerodynamic resistances using combined physics-based and machine learning, Environ. Res. Lett. 18, 034039. 10.1088/1748-9326/acbbe0, 2023.
  • Kaps, A., Lauer, A., Camps-valls, G., Gentine, P., Gómez-Chova, L., & Eyring, V. Machine-learned cloud classes from satellite data for process-oriented climate model evaluation. IEEE Transactions on Geoscience and Remote Sensing, 4100515, https://doi.org/10.1109/TGRS.2023.3237008, 2023.
  • Karmouche, S., Galytska, E., Runge, J., Meehl, G. A., Phillips, A. S., Weigel, K., and Eyring, V.: Regime-oriented causal model evaluation of Atlantic–Pacific teleconnections in CMIP6, Earth Syst. Dynam., 14, 309–344, https://doi.org/10.5194/esd-14-309-2023, 2023.
  • Galytska, E., Weigel, K., Handorf, D., Jaiser, R., Köhler, R., Runge, J., & Eyring, V.: Evaluating causal Arctic-midlatitude teleconnections in CMIP6. Journal of Geophysical Research: Atmospheres, 128, e2022JD037978. https://doi.org/10.1029/2022JD037978, 2023.
  • Schlund, M., Hassler, B., Lauer, A., Andela, B., Jöckel, P., Kazeroni, R., Loosveldt Tomas, S., Medeiros, B., Predoi, V., Sénési, S., Servonnat, J., Stacke, T., Vegas-Regidor, J., Zimmermann, K., and Eyring, V.: Evaluation of native Earth system model output with ESMValTool v2.6.0, Geosci. Model Dev., 16, 315–333, https://doi.org/10.5194/gmd-16-315-2023, 2023.

Peer-Reviewed Publications 2022

  • Behrens, G., Beucler, T., Gentine, P., Iglesias-Suarez, F., Pritchard, M., & Eyring, V. Non-Linear Dimensionality Reduction With a Variational Encoder Decoder to Understand Convective Processes in Climate Models. Journal of Advances in Modeling Earth Systems, 14, e2022MS003130, 10.1029/2022MS003130, 2022.
  • Díaz, E. Adsura, J.E., Martínez, Á.M, Piles, M., & Camps-Valls, G. Inferring causal relations from observational long-term carbon and water fluxes records. Scientific Reports. 12, 2045-2322, https://doi.org/10.1038/s41598-022-05377-7, 2022.
  • Persello, C., Wegner, J.D., Hänsch, R., Tuia, D., Ghamisi, P., Koeva, M., & Camps-Valls, G. Deep Learning and Earth Observation to Support the Sustainable Development Goals. IEEE Geoscience and Remote Sensing Magazine, 2-30, 10.1109/MGRS.2021.3136100, 2022.
  • Svendsen, D. H., Piles, M., Muñoz-Marí, J., Luengo, D., Martino, L., &, Camps-Valls G. Integrating Domain Knowledge in Data-Driven Earth Observation With Process Convolutions., IEEE Transactions on Geoscience and Remote Sensing, 60, 1-15, 10.1109/TGRS.2021.3059550, 2022.
  • Trifunov, V.T., Shadaydeh, M., & Denzler, D. Time Series Causal Link Estimation under Hidden Confounding using Knockoff Interventions. Thirty-sixth Conference on Neural Information Processing Systems (NeurIPS) Workshop 2022 “A causal view on dynamical systems workshop”, 28 Nov. – 9 Dec. 2022.
  • Grundner, A., Beucler, T., Gentine, P., Iglesias-Suarez, F., Giorgetta, M. A., & Eyring, V. Deep learning based cloud cover parameterization for ICON. Journal of Advances in Modeling Earth Systems, 14, e2021MS002959. https://doi.org/10.1029/2021MS002959, 2022.

Peer-Reviewed Publications 2021

  • Camps-Valls, G. Perspective on Deep Learning for Earth Sciences. Generalization with Deep Learning. Ch. 7, 159-173, 10.1142/9789811218842_0007, 2021.
  • Camps-Valls, G., Tuia, D., Zhu, X., X., Reichstein, M. (eds). Deep Learning for the Earth Sciences: A Comprehensive Approach to Remote Sensing, Climate Science and Geosciences. Wiley & Sons, https://github.com/DL4ES/DL4ES, 2021
  • Eyring, V., Mishra, V., Griffith, G.P., Chen, L., Keenan, T., Turetsky, M.R., Brown, S., Jotzo, F., Moore, F.C. & Van der Linden, S. Reflections and projections on a decade of climate science. Nature Clim. Change. 11, 279–285, https://doi.org/10.1038/s41558-021-01020-x, 2021.
  • Gentine, P., Eyring, V., & Beucler, T. Deep Learning for the Parametrization of Subgrid Processes in Climate Models. In Deep learning for the Earth Sciences (eds G. Camps-Valls, D. Tuia, X.X. Zhu and M. Reichstein), https://doi.org/10.1002/9781119646181.ch21, 2021.
  • Gottfriedsen, J., Berrendorf, M., Gentine, P., Hassler, B., Reichstein, M., Weigel, K., & Eyring, V. On the Generalization of Agricultural Drought Classification from Climate Data, Thirty-fifth Conference on Neural Information Processing Systems (NeurIPS) Workshop 2021 “Tackling Climate Change with Machine Learning”, 6.-14. December 2021, https://arxiv.org/abs/2111.15452
  • Kraft, B., Jung, M., Körner, M., Koirala, S., & Reichstein, M., 2021, Towards hybrid modeling of the global hydrological cycle. Hydrology and Earth System Sciences Discussions, 1-40.
  • Svendsen, D. H., Hernández-Lobato, D. , Martino, L., Laparra, V., Moreno-Martínez, A., & Camps-Valls, G. Inference over radiative transfer models using variational and expectation maximization methods. Machine Learning, 1-17, 10.1007/s10994-021-05999-4, 2021.
  • Trifunov, V., T., Shadaydeh, M., Runge, J., Reichstein, M., & Denzler, J. A Data-Driven Approach to Partitioning Net Ecosystem Exchange Using a Deep State Space Model. IEEE Access. 107873-107883, 10.1109/ACCESS.2021.3101129, 2021.
  • Trifunov, V., T., Shadaydeh, M., Barz, B., & Denzler, J. Anomaly Attribution of Multivariate Time Series using Counterfactual Reasoning. 2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA), 2021.
  • Tuia, D., Roscher, R., Wegner, J.D., Jacobs, N., Zhu, X.X., & Camps-Valls, G. Towards a Collective Agenda on AI for Earth Science Data Analysis. IEEE Geoscience and Remote Sensing Magazine, 9, 2, 88-104, 10.1109/MGRS.2020.3043504, 2021.

Peer-Reviewed Publications 2020

  • Kraft, B., Jung, M., Körner, M., & Reichstein, M., 2020, Hybrid modeling: Fusion of a deep approach and physics-based model for global hydrological modeling.The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 43, 1537-1544, 2020
  • Schlund, M., V. Eyring, G.-Camps-Valls, P. Friedlingstein, P. Gentine, & M. Reichstein, Constraining uncertainty in projected gross primary production with machine learning. JGR: Biogeosc. 125, e2019JG005619, doi:10.1029/2019JG005619, 2020.

Supporting the USMILE proposal

  • Eyring, V., Cox, P. M., Flato, G. M., Gleckler, P. J., Abramowitz, G., Caldwell, P., Collins, W. D., Gier, B., Hall, A. D., Hoffman, F. M., Hurtt, G. C., Jahn, A., Jones, C. D., Klein, S. A., Krasting, J., Kwiatkowski, L., Lorenz, R., Maloney, E., Meehl, G. A., Pendergrass, A., Pincus, R., Ruane, A. C., Russell, J. L., Sanderson, B., Santer, B., Sherwood, S. C., Simpson, I., Stouffer, R. & Williamson, M. S. Taking climate model evaluation to the next level. Nature Clim. Change 9, 102–110, 2019.
  • Gentine, P., Pritchard, M., Rasp, S., Reinaudi, G. & Yacalis, G. Could Machine Learning Break the Convection Parameterization Deadlock? Geophysical Research Letters 45, 5742-5751, doi:10.1029/2018gl078202, 2018.
  • Reichstein, M., Camps-Valls, G., Stevens, B., Denzler, J., Carvalhais, N., Jung, M. & Prabhat. Deep learning and process understanding for data-driven Earth System Science. Nature 566, 195–204, 2019.