Improved estimation of net ecosystem CO 2 exchange over North America using LSTM-based flux upscaling (2001–2021)

Accurate estimation of regional-scale terrestrial carbon budgets is of great importance but remains challenging. With particular advantages, the Long Short-Term Memory (LSTM) network method shows potential for improving regional carbon budget upscaling estimations. Here, using LSTM, we upscaled regional net ecosystem carbon exchange (NEE) with available flux tower measurements and satellite land surface observations in North America. With well-established ecosystem-specific LSTMs, we produced monthly NEE at a spatial resolution of 0.1 ° ×0.1 ° over 2001–2021 (labelled as MemoryFlux). Unlike existing upscaling estimates, our dataset properly identified the Midwest Corn Belt as a region of large seasonal carbon uptake during the peak growing season, a feature revealed by previous top-down studies and recognized as a model benchmark. Moreover, the seasonal variations in NEE estimated by MemoryFlux were strongly correlated with those from independent atmospheric inversions, including the ensemble mean of the Orbiting Carbon Observatory-2 Model Intercomparison Project (OCO-2 v10 MIP; r =0.96, p <0.001) and CarbonTracker2022 (CT2022) ( r =0.97, p <0.001). The mean annual NEE was estimated at −1.27 ± 0.12 Pg C yr −1 , which was closer in magnitude to inversions (−0.83 to −0.70 Pg C yr −1 ) than existing upscaling estimates (−3.30 to −1.68 Pg C yr −1 ). In addition, MemoryFlux captured spatial NEE anomaly patterns associated with six selected severe drought and flood events. We further found that explicitly incorporating historical predictor information improved the representation of NEE interannual variability and spatial anomalies associated with climate extremes. MemoryFlux provides an improved bottom-up estimation of North American NEE and shows greater consistency in magnitude with independent atmospheric inversion estimates than several existing EC-based upscaling products. The MemoryFlux dataset is available at https://doi.org/10.5281/zenodo.20482274 (Huang and He, 2026).