Improved estimation of net ecosystem CO2 exchange for North America in eddy flux upscaling with memory-based deep learning
- 1Zhejiang Carbon Neutral Innovation Institute, Zhejiang University of Technology, Hangzhou, China (wei.he.nju@gmail.com)
- 2School of Information Engineering, China University of Geosciences, Beijing, China
- 3College of Hydrology and Water Resources, Hohai University, Nanjing, China
- 4State Key Laboratory of Remote Sensing Science, Beijing Normal University, Beijing, China
- 5School of Geography and Tourism, Anhui Normal University, Wuhu, China
Estimation of regional-scale carbon budget still faces considerable uncertainties, whereby reconciling bottom-up estimates and top-down inversions are essential steps towards reducing such uncertainties. Here, we make full use of available flux tower observations and novel satellite land surface observations (e.g., soil moisture and solar-induced chlorophyll fluorescence) to constrain the spatiotemporal regimes of net ecosystem carbon exchange (NEE) for North America based on the advanced Long Short-term Memory (LSTM) networks. The LSTM-based model is capable of incorporating the memory effects of climate and environments on NEE variations, thus more accurately simulating the interannual variations (IAVs) and long-term trends of NEE. We produced gridded NEE at a spatial resolution of 0.1° × 0.1°and monthly time-step over 2001–2021 for North America.
The annual total NEE during 2001–2021 is -1.74 ± 0.10 Pg C yr-1, which is much closer to the top-down inversions (-0.73 and -0.63 Pg C yr-1 for CarbonTracker2022 over the same period and the Orbiting Carbon Observatory-2 (OCO-2) v10 MIP ensemble mean over 2015–2020 respectively) than the global flux upscaling products (-3.04 and -2.75 Pg C yr-1 by FLUXCOM2020 RS+CRUJRA1.1 and RS+ERA5 respectively and -3.30 Pg C yr-1 by NIES2020). Moreover, the spatial patterns matched with the OCO-2 v10 MIP ensemble mean reasonably well, which indicated the largest carbon uptake in the Midwest Corn-Belt area during peak growing seasons and in the Southeast on an annual basis. The estimated IAV of NEE is highly correlated with that by CarbonTracker2022. The LSTM estimate captured the NEE anomalies caused by the droughts in 2011, 2012, 2017, 2020–2021, and the 2019 Midwest floods. The performances are clearly better than existing global flux upscaling products. In addition, the estimated annual NEE exhibited a significant decline at the rate of -0.008 Pg C yr-2 (p < 0.05), indicating an overall enhanced carbon sink during the recent two decades, which is in line with contemporary estimates.
These results suggest that the LSTM-based NEE upscaling provides an improved estimation of North American NEE for both spatial and temporal characteristics, narrowing down the gap between bottom-up estimates and top-down inversions, which is an important step towards robust regional carbon budget estimations.
How to cite: He, W., Huang, C., Liu, J., Nguyen, N. T., Yang, H., and Zhao, M.: Improved estimation of net ecosystem CO2 exchange for North America in eddy flux upscaling with memory-based deep learning , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13485, https://doi.org/10.5194/egusphere-egu24-13485, 2024.
