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Spatio-Temporal Variations in Carbon Isotope Discrimination Predicted by the JULES Land Surface Model.

Lewis PalmerIain RobertsonAliénor LavergneDeborah HemmingNeil J LoaderGiles YoungDarren DaviesKatja Rinne-GarmstonSietse LosJamie Williams
Published in: Journal of geophysical research. Biogeosciences (2022)
Stable carbon isotopes in plants can help evaluate and improve the representation of carbon and water cycles in land-surface models, increasing confidence in projections of vegetation response to climate change. Here, we evaluated the predictive skills of the Joint UK Land Environmental Simulator (JULES) to capture spatio-temporal variations in carbon isotope discrimination (Δ 13 C) reconstructed by tree rings at 12 sites in the United Kingdom over the period 1979-2016. Modeled and measured Δ 13 C time series were compared at each site and their relationships with local climate investigated. Modeled Δ 13 C time series were significantly correlated ( p  < 0.05) with tree-ring Δ 13 C at eight sites, but JULES underestimated mean Δ 13 C values at all sites, by up to 2.6‰. Differences in mean Δ 13 C may result from post-photosynthetic isotopic fractionations that were not considered in JULES. Inter-annual variability in Δ 13 C was also underestimated by JULES at all sites. While modeled Δ 13 C typically increased over time across the UK, tree-ring Δ 13 C values increased only at five sites located in the northern regions but decreased at the southern-most sites. Considering all sites together, JULES captured the overall influence of environmental drivers on Δ 13 C but failed to capture the direction of change in Δ 13 C caused by air temperature, atmospheric CO 2 and vapor pressure deficit at some sites. Results indicate that the representation of carbon-water coupling in JULES could be improved to reproduce both the trend and magnitude of interannual variability in isotopic records, the influence of local climate on Δ 13 C, and to reduce uncertainties in predicting vegetation-environment interactions.
Keyphrases
  • climate change
  • human health
  • risk assessment
  • atomic force microscopy
  • particulate matter
  • water quality
  • air pollution