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Bios Markus Reichstein

Markus Reichstein is Director of the Biogeochemical Integration Department at the Max-Planck-Institute for Biogeochemistry. His main research interests revolve around the response and feedback of ecosystems (vegetation and soils) to climatic variability with a Earth system perspective, considering coupled carbon, water and nutrient cycles. Of specific interest is the interplay of climate extremes with ecosystem and societal resilience. These topics are adressed via a model-data integration approach, combining data-driven machine learning with systems modelling of experimental, ground- and satellite-based observations.

Since 2013 Markus Reichstein is Professor for Global Geoecology at the FSU Jena, and founding Director at the Michael-Stifel-Center Jena for Data-driven and Simulation Science. He has been serving as lead author of the IPCC special report on Climate Extremes (SREX), as member of the German Commitee Future Earth on Sustainability Research, and the Thuringian Panel on Climate. Recent awards include the Piers J. Sellers Mid-Career Award by the American Geophysical Union (2018), an ERC Synergy Grant (2019) and the Gottfried Wilhelm Leibniz Preis (2020).

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Publications 2019

1Besnard, S., Carvalhais, N., Arain, M. A., Black, A., Brede, B., Buchmann, N., Chen, J., Clevers, J. G. P. W., Dutrieux, L. P., Gans, F., Herold, M., Jung, M., Kosugi, Y., Knohl, A., Law, B. E., Paul-Limoges, E., Lohila, A., Merbold, L., Roupsard, O., Valentini, R., Wolf, S., Zhang, X., Reichstein, M. (2019). Memory effects of climate and vegetation affecting net ecosystem CO2 fluxes in global forests. PLoS One, 14(2): e0211510. doi:10.1371/journal.pone.0211510.
2Boese, S., Jung, M., Carvalhais, N., Teuling, A. J., Reichstein, M. (2019). Carbon–water flux coupling under progressive drought. Biogeosciences, 16(13), 2557-2572. doi:10.5194/bg-16-2557-2019.
3Camps-Valls, G., Sejdinovic, D., Runge, J., Reichstein, M. (2019). A perspective on Gaussian processes for Earth observation. National Science Review, 6(4), 616-618. doi:10.1093/nsr/nwz028.
4Jung, M., Koirala, S., Weber, U., Ichii, K., Gans, F., Camps-Valls, G., Papale, D., Schwalm, C., Tramontana, G., Reichstein, M. (2019). The FLUXCOM ensemble of global land-atmosphere energy fluxes. Scientific Data, 6: 74. doi:10.1038/s41597-019-0076-8.
5Kayler, Z. E., Premke, K., Gessler, A., Gessner, M. O., Griebler, C., Hilt, S., Klemedtsson, L., Kuzyakov, Y., Reichstein, M., Siemens, J., Totsche, K.-U., Tranvik, L., Wagner, A., Weitere, M., Grossart, H.-P. (2019). Integrating aquatic and terrestrial perspectives to improve insights into organic matter cycling at the landscape scale. Frontiers in Earth Science, 7: 127. doi:10.3389/feart.2019.00127.
6Keenan, T. F., Migliavacca, M., Papale, D., Baldocchi, D., Reichstein, M., Torn, M., Wutzler, T. (2019). Widespread inhibition of daytime ecosystem respiration. Nature Ecology & Evolution, 3(3), 407-415. doi:10.1038/s41559-019-0809-2.
7Knauer, J., Zaehle, S., Kauwe, M. G. D., Bahar, N. H. A., Evans, J. R., Medlyn, B. E., Reichstein, M., Werner, C. (2019). Effects of mesophyll conductance on vegetation responses to elevated CO2 concentrations in a land surface model. Global Change Biology, 25(5), 1820-1838. doi:10.1111/gcb.14604.
8Kraft, B., Jung, M., Körner, M., Requena Mesa, C., Cortés, J., Reichstein, M. (2019). Identifying dynamic memory effects on vegetation state using recurrent neural networks. Frontiers in Big Data, 2: 31. doi:10.3389/fdata.2019.00031.
9Ma, X., Mahecha, M. D., Migliavacca, M., van der Plas, F., Benavides, R., Ratcliffe, S., Kattge, J., Richter, R., Musavi, T., Baeten, L., Barnoaiea, I., Bohn, F. J., Bouriaud, O., Bussotti, F., Coppi, A., Domisch, T., Huth, A., Jaroszewicz, B., Joswig, J., Pabon-Moreno, D. E., Papale, D., Selvi, F., Laurin, G. V., Valladares, F., Reichstein, M., Wirth, C. (2019). Inferring plant functional diversity from space: the potential of Sentinel-2. Remote Sensing of Environment, 233: 111368. doi:10.1016/j.rse.2019.111368.
10Martini, D., Pacheco-Labrador, J., Perez-Priego, O., van der Tol, C., El-Madany, T. S., Julitta, T., Rossini, M., Reichstein, M., Christiansen, R., Rascher, U., Moreno, G., Martín, M. P., Yang, P., Carrara, A., Guan, J., González-Cascón, R., Migliavacca, M. (2019). Nitrogen and phosphorus effect on sun-induced fluorescence and gross primary productivity in mediterranean grassland. Remote Sensing, 11(21): 2562. doi:10.3390/rs11212562.
11Nair, R. K. F., Morris, K. A., Hertel, M., Luo, Y., Moreno, G., Reichstein, M., Schrumpf, M., Migliavacca, M. (2019). N: P stoichiometry and habitat effects on Mediterranean savanna seasonal root dynamics. Biogeosciences, 16(9), 1883-1901. doi:10.5194/bg-16-1883-2019.
12Pacheco-Labrador, J., Perez-Priego, O., El-Madany, T. S., Julitta, T., Rossini, M., Guan, J.-H., Moreno, G., Carvalhais, N., Martín, M. P., Gonzalez-Cascon, R., Kolle, O., Reichstein, M., van der Tolg, C., Carrara, A., Martini, D., Hammer, T. W., Moossen, H., Migliavacca, M. (2019). Multiple-constraint inversion of SCOPE. Evaluating the potential of GPP and SIF for the retrieval of plant functional traits. Remote Sensing of Environment, 234: 111362. doi:10.1016/j.rse.2019.111362.
13Reichstein, M., Camps-Valls, G., Stevens, B., Jung, M., Denzler, J., Carvalhais, N., Prabhat (2019). Deep learning and process understanding for data-driven Earth system science. Nature, 566(7743), 195-204. doi:10.1038/s41586-019-0912-1.
14Reichstein, M., Carvalhais, N. (2019). Aspects of forest biomass in the earth system: Its role and major unknowns. Surveys in Geophysics, 40(4), 693-707. doi:10.1007/s10712-019-09551-x.
15Reichstein, M., Frank, D., Sillmann, J., Sippel, S. (2019). Outlook: Challenges for societal resilience under climate extremes. In J. Sillmann, S. Sippel, S. Russo (Eds.), Climate extremes and their implications for impact and risk assessment (pp. 341-353). Amsterdam: Elsevier.
16Requena-Mesa, C., Reichstein, M., Mahecha, M. D., Kraft, B., Denzler, J. (2019). Predicting landscapes from environmental conditions using generative networks. In G. A. FInk, S. Frintrop, X. Jiang (Eds.), Pattern Recognition, DAGM GCPR 2019 (pp. 203-217). Cham: Springer. doi:10.1007/978-3-030-33676-9_14.
17Runge, J., Bathiany, S., Bollt, E., Camps-Valls, G., Coumou, D., Deyle, E., Glymour, C., Kretschmer, M., Mahecha, M. D., Muñoz-Marí, J., van Nes, E. H., Peters, J., Quax, R., Reichstein, M., Scheffer, M., Schölkopf, B., Spirtes, P., Sugihara, G., Sun, J., Zhang, K., Zscheischler, J. (2019). Inferring causation from time series in Earth system sciences. Nature Communications, 10: 2553. doi:10.1038/s41467-019-10105-3.
18Stoy, P. C., El-Madany, T. S., Fisher, J. B., Gentine, P., Gerken, T., Good, S. P., Klosterhalfen, A., Liu, S., Miralles, D. G., Perez-Priego, O., Rigden, A. J., Skaggs, T. H., Wohlfahrt, G., Anderson, R. G., Coenders-Gerrits, A. M. J., Jung, M., Maes, W. H., Mammarella, I., Mauder, M., Migliavacca, M., Nelson, J. A., Poyatos, R., Reichstein, M., Scott, R. L., Wolf, S. (2019). Reviews and syntheses: Turning the challenges of partitioning ecosystem evaporation and transpiration into opportunities. Biogeosciences, 16(19), 3747-3775. doi:10.5194/bg-16-3747-2019.
19Tang, X., Carvalhais, N., Moura, C., Ahrens, B., Koirala, S., Fan, S., Guan, F., Zhang, W., Gao, S., Magliulo, V., Buysse, P., Liu, S., Chen, G., Yang, W., Yu, Z., Liang, J., Shi, L., Pu, S., Reichstein, M. (2019). Global variability of carbon use efficiency in terrestrial ecosystems. Biogeosciences Discussions. doi:10.5194/bg-2019-37.
20Trifunov, V. T., Shadaydeh, M., Runge, J., Eyring, V., Reichstein, M., Denzler, J. (2019). Nonlinear causal link estimation under hidden confounding with an application to time series anomaly detection. In G. A. Fink, S. Frintrop, X. Jiang (Eds.), Pattern Recognition, DAGM GCPR 2019 (pp. 261-273). Cham: Springer. doi:10.1007/978-3-030-33676-9_18.
21Zhao, W. L., Gentine, P., Reichstein, M., Zhang, Y., Zhou, S., Wen, Y., Lin, C., Li, X., Qiu, G. Y. (2019). Physics-constrained machine learning of evapotranspiration. Geophysical Research Letters, 46(24), 14496-14507. doi:10.1029/2019GL085291.
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