The core members of the ELLIS Unite Jena have published more than 50 publications in collaboration with other ELLIS fellows or scholars.
Here we list 10 representative ones’.

ELLIS unit Jena members are in bold & italics; other ELLIS members are in bold.

1. Camps-Valls, G., Sejdinovic, D., Runge, J., and Reichstein, M.: A perspective on Gaussian processes for Earth observation, Natl Sci Rev, 6, 616-618, 10.1093/nsr/nwz028, 2019.

2. Cortés, J., Mahecha, M., Reichstein, M., and Brenning, A.: Accounting for multiple testing in the analysis of spatiotemporal environmental data, Environmental and Ecological Statistics, 10.1007/s10651-020-00446-4, 2020.

3. Cortés, J., Mahecha, M. D., Reichstein, M., Myneni, R. B., Chen, C., and Brenning, A.: Where are Global Vegetation Greening and Browning Trends Significant?, Geophys Res Lett, 48, e2020GL091496, 2021.

4. Jung, M., Reichstein, M., Schwalm, C. R., Huntingford, C., Sitch, S., Ahlström, A., Arneth, A., Camps-Valls, G., Ciais, P., Friedlingstein, P., Gans, F., Ichii, K., Jain, A. K., Kato, E., Papale, D., Poulter, B., Raduly, B., Rödenbeck, C., Tramontana, G., Viovy, N., Wang, Y.-P., Weber, U., Zaehle, S., and Zeng, N.: Compensatory water effects link yearly global land CO2 sink changes to temperature, Nature, 541, 516-520, 10.1038/nature20780, 2017.

5. Mahecha, M. D., Gans, F., Brandt, G., Christiansen, R., Cornell, S. E., Fomferra, N., Kraemer, G., Peters, J., Bodesheim, P., Camps-Valls, G., Donges, J. F., Dorigo, W., Estupinan-Suarez, L. M., Gutierrez-Velez, V. H., Gutwin, M., Jung, M., Londono, M. C., Miralles, D. G., Papastefanou, P., and Reichstein, M.: Earth system data cubes unravel global multivariate dynamics, Earth System Dynamics, 11, 201-234, 10.5194/esd-11-201-2020, 2020.

6. Reichstein, M., Camps-Valls, G., Stevens, B., Jung, M., Denzler, J., Carvalhais, N., and Prabhat: Deep learning and process understanding for data-driven Earth system science, Nature, 566, 195-204, 10.1038/s41586-019-0912-1, 2019.

7. Runge, J., Bathiany, S., Bollt, E., Coumou, D., Deyle, E., Glymour, C., Kretschmer, M., Mahecha, M. D., Munoz-Mari, J., van Nes, E. H., Peters, J., Quax, R., Reichstein, M., Scheffer, M., Schoelkopf, B., Spirtes, P., Sugihara, G., Sun, J., Zhang, K., and Zscheischler, J.: Inferring causation from time series in Earth system sciences, Nature Communications, 10, 2553, 10.1038/s41467-019-10105-3, 2019.

8. Schlund, M., Eyring, V., Friedlingstein, P., Gentine, P., and Reichstein, M.: Constraining uncertainty in projected gross primary production with machine learning, Journal of Geophysical Research: Biogeosciences, 10.1029/2019jg005619, 2020.

9. Tramontana, G., Migliavacca, M., Jung, M., Reichstein, M., Keenan, T. F., Camps‐Valls, G., Ogee, J., Verrelst, J., and Papale, D.: Partitioning net carbon dioxide fluxes into photosynthesis and respiration using neural networks, Global Change Biology, 10.1111/gcb.15203, 2020.

10. Trifunov, V. T., Shadaydeh, M., Runge, J., Eyring, V., Reichstein, M., and Denzler, J.: Nonlinear causal link estimation under hidden confounding with an application to time series anomaly detection, in: Pattern Recognition, DAGM GCPR 2019, edited by: Fink, G. A., Frintrop, S., and Jiang, X., Lecture Notes in Computer Science, 11824, Springer, Cham, 261-273, 2019.