Publications of Christian Reimers

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)

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)

Wang, Lijun, L.; Shi, L.; Reimers, C.; Wang, Y.; He, L.; Wang, Y.; Reichstein, M.; Jiang, S.: A self-supervised deep learning model for enhanced generalization in soil moisture prediction. Journal of Hydrology 662 (Part B), 133974 (2025)

Yu, X.; Orth, R.; Reichstein, M.; Reimers, C.; Gomarasca, U.; Migliavacca, M.; Papale, D.; Bahn, M.; Bastos, A.: Widespread but divergent drought legacy effects on gross primary productivity across biomes. Global Change Biology 31 (10), e7054 (2025)

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)

Liu, G.; Migliavacca, M.; Reimers, C.; Kraft, B.; Reichstein, M.; Richardson, A. D.; Wingate, L.; Delpierre, N.; Yang, H.; Winkler, A.: DeepPhenoMem V1.0: Deep learning modelling of canopy greenness dynamics accounting for multi-variate meteorological memory effects on vegetation phenology. Geoscientific Model Development 17 (17), pp. 6683 - 6701 (2024)

Winkler, A.; Myneni, R.; Reimers, C.; Reichstein, M.; Brovkin, V.: Carbon system state determines warming potential of emissions. PLOS ONE 19 (8), e0306128 (2024)

Wutzler, T.; Reimers, C.; Ahrens, B.; Schrumpf, M.: Optimal enzyme allocation leads to the constrained enzyme hypothesis: the Soil Enzyme Steady Allocation Model (SESAM; v3.1)). Geoscientific Model Development 17 (7), pp. 2705 - 2725 (2024)

Reimers, C.; Bodesheim, P.; Runge, J.; Denzler, J.: Conditional adversarial debiasing: Towards learning unbiased classifiers from biased data. In: Pattern Recognition. DAGM GCPR 2021. Lecture Notes in Computer Science, Vol. 13024, pp. 48 - 62. Springer International Publishing, Cham (2021)

Friede, D.; Reimers, C.; Stuckenschmidt, H.; Niepert, M.: Learning disentangled discrete representations. Machine learning and knowledge discovery in databases: Research track. ECML PKDD 2023. Lecture Notes in Computer Science 14172, pp. 593 - 609 (2023)

Winkler, A. J.; Kranz, J.; Graf, A.; Caporaso, L.; Duveiller, G.; Fan, N.; Gharun, M.; Green, J. K.; Hammerle, A.; Harrison, S. P. et al.; Hau, O.; Javadian, M.; Li, X.; Guohua, L.; Mauder, M.; Migliavacca, M.; Miralles, D. G.; Hafezi Rachti, D.; 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)

Zhao, W.; Zhang, S.; Fang, J.; Asadollahi, M.; Reimers, C.; Meier, R.; Qiu, G. Y.; Gentine, P.: Multi-scale memory governs ground heat flux dynamics. SSRN eLibrary (accepted)

Xuan, N.; Zhao, W.; Reimers, C.; Jiang, J.; Zhu, B.; Hu, W.; Zhang, C.; Guo, H.; Zhang, S.; Xiong, Y. et al.; Qiu, G. Y.: Unveiling the carbon emission mechanism of global power system under extreme weather: Country-specific insights through explainable machine learning. SSRN eLibrary (2025)

Reimers, C.; Hafezi Rachti, D.; Liu, G.; Winkler, A.: Comparing data-driven and mechanistic models for predicting phenology in deciduous broadleaf forests. arXiv (2024)