Preprints
https://doi.org/10.5194/esd-2021-38
https://doi.org/10.5194/esd-2021-38

  10 Jun 2021

10 Jun 2021

Review status: this preprint is currently under review for the journal ESD.

Process-based analysis of terrestrial carbon flux predictability

István Dunkl1,2, Aaron Spring1, Pierre Friedlingstein3, and Victor Brovkin1,4 István Dunkl et al.
  • 1Max Planck Institute for Meteorology, Hamburg, Germany
  • 2International Max Planck Research School on Earth System Modelling, Hamburg, Germany
  • 3College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, UK
  • 4Center for Earth System Research and Sustainability, University of Hamburg, Germany

Abstract. Despite efforts to decrease the discrepancy between simulated and observed terrestrial carbon fluxes, the uncertainty in trends and patterns of the land carbon fluxes remains high. This difficulty raises the question to what extent the terrestrial carbon cycle is predictable, and which processes explain the predictability. Here, the perfect model approach is used to assess the potential predictability of net primary production (NPPpred) and heterotrophic respiration (Rhpred) by using ensemble simulations conducted with the Max-Planck-Institute Earth System Model. In order to asses the role of local carbon flux predictability (CFpred) on the predictability of the global carbon cycle, we suggest a new predictability metric weighted by the amplitude of the flux anomalies. Regression analysis is used to determine the contribution of the predictability of different environmental drivers to NPPpred and Rhpred (soil moisture, air temperature and radiation for NPP and soil organic carbon, air temperature and precipitation for Rh). NPPpred is driven to 62 and 30 % by the predictability of soil moisture and temperature, respectively. Rhpred is driven to 52 and 27 % by the predictability of soil organic carbon temperature, respectively. The decomposition of predictability shows that the relatively high Rhpred compared to NPPpred is due to the generally high predictability of soil organic carbon. The seasonality in NPPpred and Rhpred patterns can be explained by the change in limiting factors over the wet and dry months. Consequently, CFpred is controlled by the predictability of the currently limiting environmental factor. Differences in CFpred between ensemble simulations can be attributed to the occurrence of wet and dry years, which influences the predictability of soil moisture and temperature. This variability of predictability is caused by the state dependency of ecosystem processes. Our results reveal the crucial regions and ecosystem processes to be considered when initializing a carbon prediction system.

István Dunkl et al.

Status: open (until 14 Aug 2021)

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István Dunkl et al.

István Dunkl et al.

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Short summary
The variability in atmospheric CO2 is largely controlled by terrestrial carbon fluxes. These land-atmosphere fluxes are predictable for around two years, but the mechanisms providing the predictability are not well understood. By decomposing the predictability of carbon fluxes into individual contributors we were able to explain the spatial and seasonal patterns, and the interannual variability of predictability.
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