Publications

  1. Zhan, C., Orth, R., Yang, H., Reichstein, M., Zaehle, S., De Kauwe, M. G., et al.: Estimating the CO2 fertilization effect on extratropical forest productivity from flux-tower observations. Journal of Geophysical Research: Biogeosciences, 129 (2024)
  2. Son, R., Stacke, T., Gayler, V., Nabel, JEMS., Schnur, R., Alonso, L., et al.: Integration of a Deep-Learning-Based Fire Model Into a Global Land Surface Model. Journal of Advances in Modeling Earth Systems, 16(1) (2024)
  3. Liu, G., Migliavacca, M., Reimers, C., Kraft, B., Reichstein, M., Richardson, A. D., Wingate, L., Delpierre, N., Yang, H., and Winkler, A. J.: DeepPhenoMem V1.0: deep learning modelling of canopy greenness dynamics accounting for multi-variate meteorological memory effects on vegetation phenology, Geosci. Model Dev., 17, pp. 6683–6701 (2024)
  4. ElGhawi, R., Kraft, B., Reimers, C., Reichstein, M., Körner, M., Gentine P, et al.: Hybrid modeling of evapotranspiration: inferring stomatal and aerodynamic resistances using combined physics-based and machine learning. Environ Res Lett., 18(3):034039 (2023)
  5. Kyker-Snowman, E., et al.: Increasing the spatial and temporal impact of ecological research: A roadmap for integrating a novel terrestrial process into an Earth system model.. Global Change Biology, 28, pp. 665-684 (2022)
  6. Tang C., Uriarte, M., Jin, H., Morton, D. and Zheng, T.: Large-Scale; Image-Based Tree Species Mapping in a Tropical Forest using Artificial Perceptual Learning. Methods in Ecology and Evolution, 12 (4), pp. 608-618 (2021)
  7. E. Cleary, A. Garbuno-Inigo, S. Lan, T. Schneider, and A. M. Stuart: Calibrate, emulate, sample. J. Comp. Phys., 424 (2021)
  8.  Guo, J., Yuan, C., Ning, S., Zheng, T., Bello, N., Kiryluk, K., and Weng, C.: Similarity-based Health Risk Prediction Using Domain Fusion and Electronic Health Records Data. Journal of Biomedical Informatics, 116 (2021)

  9. Humphrey, V., Berg, A., Ciais, P., Gentine, P., Jung, M., Reichstein, M., Seneviratne, S. I., and Frankenberg, C.: Soil moisture–atmosphere feedback dominates land carbon uptake variability. Nature, 592 (2021)

  10. Schneider, T., Jeevanjee, N, and Socolow, R.: Accelerating progress in climate science. Physics Today, 74, pp. 44-51 (2021)

  11. Sun, W., et al.: Midwest U.S. croplands determine model divergence in North American carbon fluxes. AGU Advances, 2 (2021)

  12. Randazzo, N.A., et al.: Higher autumn temperatures lead to contrasting CO2 flux responses in boreal forests versus tundra and shrubland. Geophysical Research Letters, 48 (2021)

  13. Reichstein, M., Camps-Valls, G., Stevens, B., Jung, M., Denzler, J., Carvalhais, N., Prabhat: Deep learning and process understanding for data-driven Earth system science. Nature, 566 (7743), pp. 195-204 (2019)

The MC³ 4 Earth Center is a collaboration of the Max-Planck Institute of Biogeochemistry, Max-Planck-Institute for Chemistry, California Institute of Technology, Carnegie Institution for Science (Department of Global Ecology) and Columbia University. It's coordination and Max-Planck research is funded by the Max Planck Förderstiftung.

Contact: Dora Kelemen (dkelemen@bgc-jena.mpg.de)

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