Computer Science > Machine Learning
arXiv:2603.09974 (cs)
[Submitted on 10 Mar 2026]
Title:Task Aware Modulation Using Representation Learning for Upsaling of Terrestrial Carbon Fluxes
View a PDF of the paper titled Task Aware Modulation Using Representation Learning for Upsaling of Terrestrial Carbon Fluxes, by Aleksei Rozanov and 2 other authors
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Abstract:Accurately upscaling terrestrial carbon fluxes is central to estimating the global carbon budget, yet remains challenging due to the sparse and regionally biased distribution of ground measurements. Existing data-driven upscaling products often fail to generalize beyond observed domains, leading to systematic regional biases and high predictive uncertainty. We introduce Task-Aware Modulation with Representation Learning (TAM-RL), a framework that couples spatio-temporal representation learning with knowledge-guided encoder-decoder architecture and loss function derived from the carbon balance equation. Across 150+ flux tower sites representing diverse biomes and climate regimes, TAM-RL improves predictive performance relative to existing state-of-the-art datasets, reducing RMSE by 8-9.6% and increasing explained variance ($R^2$) from 19.4% to 43.8%, depending on the target flux. These results demonstrate that integrating physically grounded constraints with adaptive representation learning can substantially enhance the robustness and transferability of global carbon flux estimates.
| Comments: | |
| Subjects: | Machine Learning (cs.LG); Atmospheric and Oceanic Physics (physics.ao-ph) |
| Cite as: | arXiv:2603.09974 [cs.LG] |
| (or arXiv:2603.09974v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2603.09974
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View a PDF of the paper titled Task Aware Modulation Using Representation Learning for Upsaling of Terrestrial Carbon Fluxes, by Aleksei Rozanov and 2 other authors
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