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El Niño-Southern Oscillation forcing on carbon and water cycling in a Bornean tropical rainforest.

Naoya TakamuraYoshiaki HataKazuho MatsumotoTomonori KumeMasahito UeyamaTomo'omi Kumagai
Published in: Proceedings of the National Academy of Sciences of the United States of America (2023)
Carbon dioxide and water vapor exchanges between tropical forest canopies and the atmosphere through photosynthesis, respiration, and evapotranspiration (ET) influence carbon and water cycling at the regional and global scales. Their inter- and intra-annual variations are sensitive to seasonal rhythms and longer-timescale tropical climatic events. In the present study, we assessed the El Niño-Southern Oscillation (ENSO) influence on ET and on the net ecosystem exchange (NEE), using eddy-covariance flux observations in a Bornean rainforest over a 10-y period (2010-2019) that included several El Niño and La Niña events. From flux model inversions, we inferred ecophysiological properties, notably the canopy stomatal conductance and "big-leaf" maximum carboxylation rate ( V cmax25_BL ). Mean ET values were similar between ENSO phases (El Niño, La Niña, and neutral conditions). Conversely, the mean net ecosystem productivity was highest during La Niña events and lowest during El Niño events. Combining Shapley additive explanation calculations for nine controlling factors with a machine-learning algorithm, we determined that the primary factors for ET and NEE in the La Niña and neutral phases were incoming shortwave solar radiation and V cmax25_BL , respectively, but that canopy stomatal conductance was the most significant factor for both ET and NEE in the El Niño phase. A combined stomatal-photosynthesis model approach further indicated that V cmax25_BL differences between ENSO phases were the most significant controlling factor for canopy photosynthesis, emphasizing the strong need to account for ENSO-induced ecophysiological factor variations in model projections of the long-term carbon balance in Southeast Asian tropical rainforests.
Keyphrases
  • climate change
  • machine learning
  • metal organic framework
  • transition metal
  • carbon dioxide
  • high frequency
  • big data
  • oxidative stress
  • radiation induced
  • diabetic rats