Researcher Portfolio

Marra, Daniel Magnabosco

Department Biogeochemical Processes, Prof. S. E. Trumbore, Max Planck Institute for Biogeochemistry, Max Planck Society, IMPRS International Max Planck Research School for Global Biogeochemical Cycles, Max Planck Institute for Biogeochemistry, Max Planck Society, Interdepartmental Max Planck Fellow Group Functional Biogeography, Max Planck Institute for Biogeochemistry, Max Planck Society

Researcher Profile

Position: IMPRS International Max Planck Research School for Global Biogeochemical Cycles, Max Planck Institute for Biogeochemistry, Max Planck Society

Position: Department Biogeochemical Processes, Prof. S. E. Trumbore, Max Planck Institute for Biogeochemistry, Max Planck Society

Position: Interdepartmental Max Planck Fellow Group Functional Biogeography, Max Planck Institute for Biogeochemistry, Max Planck Society

Researcher ID: https://pure.mpg.de/cone/persons/resource/persons128355

External references

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Publications

(1 - 25 of 114)

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: Poehls, J., Alonso, L., Koirala, S., Carvalhais, N., & Reichstein, M. (2025). Downscaling soil moisture to sub-km resolutions with simple machine learning ensembles. Journal of Hydrology, 652: 132624. doi:10.1016/j.jhydrol.2024.132624. [PubMan] : De, R., Brenning, A., Reichstein, M., Sigut, L., Reverter, B. R., Korkiakoski, M., Paul-Limoges, E., Blanken, P. D., Black, T. A., Gielen, B., Tagesson, T., Wohlfahrt, G., Montagnani, L., Wolf, S., Chen, J., Liddell, M., Desai, A., Koirala, S., & Carvalhais, N. (2025). Inter–annual variability of hydrological parameters improves simulation of annual gross primary production. ESS Open Archive. doi:10.22541/essoar.174349993.30198378/v1. [PubMan] : Karasante, I., Alonso, L., Prapas, I., Ahuja, A., Carvalhais, N., & Papoutsis, I. (2025). SeasFire cube - a multivariate dataset for global wildfire modeling. Scientific Data, 12: 368. doi:10.1038/s41597-025-04546-3. [PubMan] : Lee, H. T., Jung, M., Carvalhais, N., Reichstein, M., Forkel, M., Bloom, A. A., Pacheco-Labrador, J., & Koirala, S. (2025). Spatial attribution of temporal variability in global land-atmosphere CO₂ exchange using a model-data integration framework. Journal of Advances in Modeling Earth Systems, 17(3): e2024MS004479v. doi:10.1029/2024MS004479. [PubMan] : Neigh, C. S. R., Montesano, P. M., Sexton, J. O., Wooten, M., Wagner, W., Feng, M., Carvalhais, N., Calle, L., & Carroll, M. L. (2025). Russian forests show strong potential for young forest growth. Communications Earth & Environment, 6: 71. doi:10.1038/s43247-025-02006-9. [PubMan] : Benson, V., Robin, C., Requena Mesa, C., Alonso, L., Carvalhais, N., Cortés, J., Gao, Z., Linscheid, N., Weynants, M., & Reichstein, M. (2024). Multi-modal learning for geospatial vegetation forecasting. In 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). doi:10.1109/CVPR52733.2024.02625. [PubMan] : Raoult, N., Douglas, N., MacBean, N., Kolassa, J., Quaife, T., Roberts, A. G., Fisher, R. A., Fer, I., Bacour, C., Dagon, K., Hawkins, L., Carvalhais, N., Cooper, E., Dietze, M., Gentine, P., Kaminski, T., Kennedy, D., Liddy, H. M., Moore, D., Peylin, P., Pinnington, E., Sanderson, B. M., Scholze, M., Seiler, C., Smallman, T. L., Vergopolan, N., Viskari, T., Williams, M., & Zobitz, J. (2024). Parameter estimation in land surface models: Challenges and opportunities with data assimilation and machine learning. ESS Open Archive. doi:10.22541/essoar.172838640.01153603/v1. [PubMan] : De, R., Bao, S., Koirala, S., Brenning, A., Reichstein, M., Tagesson, T., Liddell, M., Ibrom, A., Wolf, S., Sigut, L., Hörtnagl, L., Woodgate, W., Korkiakoski, M., Merbold, L., Black, T. A., Roland, M. E., Klosterhalfen, A., Blanken, P. D., Knox, S., Sabbatini, S., Gielen, B., Montagnani, L., Fensholt, R., Wohlfahrt, G., Desai, A. R., Paul-Limoges, E., Galvagno, M., Hammerle, A., Jocher, G., Reverter, B. R., Holl, D., Chen, J., Vitale, L., Arain, M. A., & Carvalhais, N. (2024). Addressing challenges in simulating inter-annual variability of gross primary production. ESS Open Archive. doi:10.22541/essoar.172656939.93739740/v1. [PubMan] : Cohrs, K.-H., Varando, G., Camps-Valls, G., Carvalhais, N., & Reichstein, M. (2024). Causal hybrid modeling with double machine learning—applications in carbon flux modeling. Machine Learning: Science and Technology, 5(3): 035021. doi:10.1088/2632-2153/ad5a60. [PubMan] : Wang, S., Yang, H., Koirala, S., Forkel, M., Reichstein, M., & Carvalhais, N. (2024). Understanding disturbance regimes from patterns in modeled forest biomass. Journal of Advances in Modeling Earth Systems, 16(6): e2023MS004099. doi:10.1029/2023MS004099. [PubMan] : Dinh, T. L. A., Goll, D., Ciais, P., Carvalhais, N., & Lauerwald, R. (2024). Benchmarking simulations of forest regrowth across Europe. In EGU General Assembly 2024. doi:10.5194/egusphere-egu24-1784. [PubMan] : Bao, S., Carvalhais, N., Xu, J., Chen, J., Lei, Y., Tana, G., Lin, C., & Shi, J. (2024). Global distribution pattern in characteristics of gross primary productivity response to soil water availability. SSRN Research Paper Series. doi:10.2139/ssrn.4789075. [PubMan] : Yang, H., Wang, S., Son, R., Lee, H. T., Benson, V., Zhang, W., Zhang, Y., Zhang, Y., Kattge, J., Boenisch, G., Schepaschenko, D., Karaszewski, Z., Stereńczak, K., Moreno-Martínez, Á., Nabais, C., Birnbaum, P., Vieilledent, G., Weber, U., & Carvalhais, N. (2024). Global patterns of tree wood density. Global Change Biology, 30(3): e17224. doi:10.1111/gcb.17224. [PubMan] : Tao, F., Houlton, B. Z., Frey, S. D., Lehmann, J., Manzoni, S., Huang, Y., Jiang, L., Mishra, U., Hungate, B. A., Schmidt, M. W. I., Reichstein, M., Carvalhais, N., Ciais, P., Wang, Y.-P., Ahrens, B., Hugelius, G., Hocking, T. D., Lu, X., Shi, Z., Viatkin, K., Vargas, R., Yigini, Y., Omuto, C., Malik, A. A., Peralta, G., Cuevas-Corona, R., Paolo, L. E. D., Luotto, I., Liao, C., Liang, Y.-S., Saynes, V. S., Huang, X., & Luo, Y. (2024). Reply to: Model uncertainty obscures major driver of soil carbon. Nature, 627, E4-E6. doi:10.1038/s41586-023-07000-9. [PubMan] : Son, R., Stacke, T., Gayler, V., Nabel, J. E. M. S., Schnur, R., Alonso, L., Requena Mesa, C., Winkler, A., Hantson, S., Zaehle, S., Weber, U., & Carvalhais, N. (2024). Integration of a deep-learning-based fire model into a global land surface model. Journal of Advances in Modeling Earth Systems, 16(1): e2023MS003710. doi:10.1029/2023MS003710. [PubMan] : Yang, H., Stereńczak, K., Karaszewski, Z., & Carvalhais, N. (2023). Similar importance of inter-tree and intra-tree variations in wood density observations in Central Europe. EGUsphere. doi:10.5194/egusphere-2023-2691. [PubMan] : Voigt, H., Carvalhais, N., Meuschke, M., Reichstein, M., Zarrie, S., & Lawonn, K. (2023). VIST5: An adaptive, retrieval-augmented language model for visualization-oriented dialog. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: System Demonstrations (pp. 70-81). Singapur: Association for Computational Linguistics. doi:10.18653/v1/2023.emnlp-demo.5. [PubMan] : Fan, N., Santoro, M., Besnard, S., Cartus, O., Koirala, S., & Carvalhais, N. (2023). Implications of the steady-state assumption for the global vegetation carbon turnover. Environmental Research Letters, 18(10): 104036. doi:10.1088/1748-9326/acfb22. [PubMan] : Bao, S., Alonso, L., Wang, S., Gensheimer, J., De, R., & Carvalhais, N. (2023). Toward robust parameterizations in ecosystem‐level photosynthesis models. Journal of Advances in Modeling Earth Systems, 15(8): e2022MS003464. doi:10.1029/2022MS003464. [PubMan] : Tao, F., Houlton, B. Z., Frey, S. D., Lehmann, J., Manzoni, S., Huang, Y., Jiang, L., Mishra, U., Hungate, B. A., Schmidt, M. W. I., Reichstein, M., Carvalhais, N., Ciais, P., Wang, Y.-P., Ahrens, B., Hugelius, G., Hocking, T. D., Lu, X., Shi, Z., Viatkin, K., Vargas, R., Yigini, Y., Omuto, C., Malik, A. A., Peralta, G., Cuevas-Corona, R., Paolo, L. E. D., Luotto, I., Liao, C., Liang, Y.-S., Saynes, V. S., Huang, X., & Luo, Y. (2023). Reply to: Contribution of carbon inputs to soil carbon accumulation cannot be neglected. bioRxiv: the preprint server for biology. doi:10.1101/2023.08.20.552557. [PubMan] : Tao, Feng, F., Huang, Y., Hungate, B. A., Manzoni, S., Frey, S. D., Schmidt, M. W. I., Reichstein, M., Carvalhais, N., Ciais, P., Jiang, L., Lehmann, J., Wang, Y.-P., Houlton, B. Z., Ahrens, B., Mishra, U., Hugelius, G., Hocking, T. D., Lu, X., Shi, Z., Viatkin, K., Vargas, R., Yigini, Y., Omuto, C., Malik, A. A., Peralta, G., Cuevas-Corona, R., Di Paolo, L. E., Luotto, I., Liao, C., Liang, Y.-S., Saynes, V. S., Huang, X., & Luo, Y. (2023). Microbial carbon use efficiency promotes global soil carbon storage. Nature, 618, 981-985. doi:10.1038/s41586-023-06042-3. [PubMan] : Pacheco-Labrador, J., de Bello, F., Migliavacca, M., Ma, X., Carvalhais, N., & Wirth, C. (2023). A generalizable normalization for assessing plant functional diversity metrics across scales from remote sensing. Methods in Ecology and Evolution, 14(8), 2123-2136. doi:10.1111/2041-210X.14163. [PubMan] : Lee, H. T., Jung, M., Carvalhais, N., Trautmann, T., Kraft, B., Reichstein, M., Forkel, M., & Koirala, S. (2023). Diagnosing modeling errors in global terrestrial water storage interannual variability. Hydrology and Earth System Sciences, 27(7), 1531-1563. doi:10.5194/hess-27-1531-2023. [PubMan] : Yang, H., Munson, S. M., Huntingford, C., Carvalhais, N., Knapp, A. K., Li, X., Peñuelas, J., Zscheischler, J., & Chen, A. (2023). The detection and attribution of extreme reductions in vegetation growth across the global land surface. Global Change Biology, 29(8), 2351-2362. doi:10.1111/gcb.16595. [PubMan] : Zhang, W., Jung, M., Migliavacca, M., Poyatos, R., Miralles, D. G., El-Madany, T. S., Galvagno, M., Carrara, A., Arriga, N., Ibrom, A., Mammarella, I., Papale, D., Cleverly, J. R., Liddell, M., Wohlfahrt, G., Markwitz, C., Mauder, M., Paul-Limoges, E., Schmidt, M., Wolf, S., Brümmer, C., Arain, M. A., Fares, S., Kato, T., Ardö, J., Oechel, W., Hanson, C., Korkiakoski, M., Biraud, S., Steinbrecher, R., Billesbach, D., Montagnani, L., Woodgate, W., Shao, C., Carvalhais, N., Reichstein, M., & Nelson, J. A. (2023). The effect of relative humidity on eddy covariance latent heat flux measurements and its implication for partitioning into transpiration and evaporation. Agricultural and Forest Meteorology, 330: 109305. doi:10.1016/j.agrformet.2022.109305. [PubMan]