Publications

  1. Tan, J., Su, H., Carmichael, G., Fu, J., Li, L., Cheng, Y.: Organic Nitrogen Deposition: A Critical yet Overlooked Component of China’s Nitrogen-Carbon Equilibrium, Nature Communications. (under review)
  2. Deck, K., Braghiere, R. K., Renchon, A.A., Sloan, J., Bozzola, G., Speer, E., et al.: ClimaLand: A land surface model designed to enable data‐driven parameterizations. Journal of Advances in Modeling Earth Systems, 18, e2025MS005118 (2026)
  3. Dukes, J.S., Xu, C., Liao, C., Novick, K.A., Phillips, R.P., Beverly, D.P., Fang, Y., Jacobs, E.M., McAdam, S.A.M., Paudel, I., Rimer, I.M. and Robbins, Z.J.: Improving the representation of plant water stress and water use in Earth System Models. New Phytol, 249: 39-55 (2026)
  4. Neira, M., P. Georgiades, Y. Proestos, T. Economou, J. Araya, S. Malas, M. Omirou, D. Sparaggis, G. Hadjipavlou, and J. Lelieveld: Climate change and thermal stress in cattle: Global projections with high temporal resolution, PLOS Climate 5, e0000761 (2026)
  5. Winkler, A.J., Kranz, J., Graf, A., Caporaso, L., Duveiller, G., Fan, N., Gharun, M., Green, J.K., Hammerle, A., Harrison, S.P., Hau, O., Javadian, M., Li, X., Liu, G., Mauder, M., Migliavacca, M., Miralles, D.G., Rachti, D.H., Panwar, A., Reichstein, M., Reimers, C., Ribeiro, A.F.S., Richardson, A.D., Wohlfahrt, G., Yerba, M., Zhao, L., Forkel, M.: The Phenology-Climate Feedback Loop: Current Understanding and Future Directions. ESS Open Archive (2026)
  6. Benson, V., Bastos, A., Reimers, C., Winkler, A., Yang, F., Reichstein, M.: Atmospheric transport modeling of CO2 with neural networks. Journal of Advances in Modeling Earth Systems 17 (2), e2024MS004655 (2025)
  7.  ElGhawi, R., Reimers, C., Schnur, R., Reichstein, M., Körner, M., Carvalhais, N., Winkler, A. J.: Hybrid‐modeling of land‐atmosphere fluxes using integrated machine learning in the ICON‐ESM modeling framework. Journal of Advances in Modeling Earth Systems 17 (12), e2025MS005102 (2025)
  8. ElGhawi, R.; Winkler, A.; Reimers, C.; Schall, A.; Gensheimer, J.; Kraft, B.: Imitation or identification: limitations of deep learning in extrapolating to future climate-carbon cycle change. Machine Learning: Earth 1 (1), 01LT02 (2025)
  9. Krüger, M., T. Galeazzo, I. Eremets, B. Schmidt, U. Pöschl, M. Shiraiwa, T. Berkemeier: Improved vapor pressure predictions using group contribution-assisted graph convolutional neural networks (GC2NN). Geoscientific Model Devolopment, 18 (2025)
  10. Milousis, A., Klingmüller, K., Tsimpidi, A. P., Kok, J. F., Kanakidou, M., Nenes, A., and Karydis, V. A.: Impact of mineral dust on the global nitrate aerosol direct and indirect radiative effect. Atmos. Chem. Phys., 25, 1333–1351 (2025)
  11. Pu, J., Chang, Y., Winkler, A.J., Zuo, Z., Chen, C., Knyazikhin, Y., Myneni, R.B.: Large gains in leaf scale photosynthetic rates of sparsely vegetated arid and semi-arid lands. Communications Earth & Environment, 7 (2025)
  12. Predybaylo, E., Lelieveld, J., Pozzer, A., Gromov, S., Zimmermann, P., Osipov, S., Klingmüller, K., Steil, B., Stenchikov, G., McCabe, M.: Surface temperature and ozone responses to the 2030 Global Methane Pledge. Climatic Change, 178 (2025)
  13. Wang, Y., Braghiere, R.K., Fischer, W.W., Yao, Y., Shen, Z., Schneider, T., Bloom, A.A., Schimel, D., Croft, H., Winkler, A.J., Reichstein, M., Frankenberg, C.: Impacts of leaf traits on vegetation optical properties in Earth system modeling. Nature Communications 16, 4968 (2025)
  14. Winkler, A.; Sierra, C.: Towards a new generation of impulse‐response functions for integrated earth system understanding and climate change attribution. Geophysical Research Letters 52 (2025)
  15. Zhan, W., Lian, X., Liu, J., Han, J., Huang, Y., Yang, H., Zhan, C., Winkler, A.J., Gentine, P.: Reduced water loss rather than increased photosynthesis controls CO2-enhanced water-use efficiency. Nature Ecology and Evolution 1–14 (2025)
  16. Zhang, S., Zhao, W., Zhu, B. et al.: A near-real time daily European Power Consumption and Carbon Intensity Dataset (ECON-PowerCI). Sci Data 12, 1693 (2025)
  17. Zhao, J., Sun, S., Yin, Y., Tang, Y., Li, C., Liang, Y., Wang, Y., Winkler, A., Jiang, S.: Advancing Evapotranspiration Modeling With Optimized Soil and Canopy Resistance Combinations. Water Resources Research 61, e2024WR039252 (2025)
  18. De Vries, A.J., Armon, M., Klingmüller, K., Portmann, R., Röthlisberger, M., Domeisen, D.I.V.: Breaking Rossby waves drive extreme precipitation in the world’s arid regions. Commun Earth Environ, 5 (2024)
  19. Krüger, M., A. Mishra, P. Spichtinger, U. Pöschl, T. Berkemeier: A numerical compass for experiment design in chemical kinetics and molecular property estimation. Journal of Cheminformatics, 16 (2024)
  20. 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)
  21. Ma, C., Ni, R., Su, H., and Cheng, Y.: Enhancing Global Simulation of Smoke Injection Height for Intense Pyro-Convection Through Coupling an Improved One-Dimensional Plume Rise Model in CAM-chem, J. Adv. Model. Earth Syst., 16, e2023MS004127 (2024)
  22. Martin, A., Gayler, V., Steil, B., Klingmüller, K., Jöckel, P., Tost, H., Lelieveld, J., and Pozzer, A.: Evaluation of the coupling of EMACv2.55 to the land surface and vegetation model JSBACHv4, Geoscientific Model Development, 17, pp. 5705–5732 (2024)
  23. 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)
  24. 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)
  25. Berkemeier, T., M. Krüger, A. Feinberg, M. Müller, U. Pöschl, U. K. Krieger: Accelerating models for multiphase chemical kinetics through machine learning with polynomial chaos expansion and neural networks. Geoscientific Model Development, 16 (7), pp. 2037-2054 (2023)
  26. 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)
  27. Krüger, M., J. Wilson, M. Wietzoreck, B. A. M. Bandowe, G. Lammel, B. Schmidt, U. Pöschl,  T. Berkemeier: Convolutional neural network prediction of molecular properties for aerosol chemistry and health effects. Natural Sciences 2 (2022)
  28. 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)
  29. E. Cleary, A. Garbuno-Inigo, S. Lan, T. Schneider, and A. M. Stuart: Calibrate, emulate, sample. J. Comp. Phys., 424 (2021)
  30. 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)
  31. 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)
  32. 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)
  33. Schneider, T., Jeevanjee, N, and Socolow, R.: Accelerating progress in climate science. Physics Today, 74, pp. 44-51 (2021)
  34. Sun, W., et al.: Midwest U.S. croplands determine model divergence in North American carbon fluxes. AGU Advances, 2 (2021)
  35. 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)
  36. 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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