Publications

Zhang, WQ; Luo, GP; Hamdi, R; Ma, XM; Termonia, P; De Maeyer, P; Chen, AP (2025). Bridging the gap in carbon cycle studies: Meteorological station-based carbon flux dataset as a complement to EC towers. AGRICULTURAL AND FOREST METEOROLOGY, 362, 110397.

Abstract
The scarcity and uneven global distribution of eddy covariance (EC) towers are the key factors that contribute to significant uncertainties in carbon cycle studies of terrestrial ecosystems. To address this limitation of EC towers, Zhang et al. (2023b) developed a meteorological station-based net ecosystem exchange (NEE) dataset. This dataset includes 4674 global meteorological stations, representing a 22-fold increase compared to the 212 existing EC towers and covering a broader range of ecosystem types. Here, we propose a systematic framework for the comprehensive assessment of spatio-temporal representativeness and global uncertainty of the meteorological station-based carbon flux dataset. Meteorological stations effectively enhance the spatial representativeness of the EC towers and reduce the latitudinal variability of the spatial representativeness. In most regions, the temporal trends of carbon flux data from meteorological stations did not significantly differ from those observed by EC towers (p < 0.001). The global uncertainty of carbon fluxes from meteorological station is 0.37, followed by the VISIT and FLUXCOM products with uncertainties of 0.44 and 0.45, respectively. Overall, the carbon fluxes from meteorological stations exhibit higher spatial representativeness and better temporal representativeness compared to the EC tower observations and possess lower global uncertainties than the existing carbon flux gridded products. Consequently, the carbon flux data derived from meteorological stations is a trade-off dataset that addresses the low spatial representativeness of the EC towers and the high uncertainty of the gridded products. It effectively complements the existing EC tower data while ensuring accuracy. The development of this dataset will play an important role in reducing the uncertainty of global carbon sink-related studies.

DOI:
10.1016/j.agrformet.2025.110397

ISSN:
1873-2240