CIML-TER v1.0 (2001-2020)

Accurate estimation of terrestrial ecosystem respiration (TER) is essential for refining global carbon budgets and informing climate change response policies. Current large-scale TER models predominantly rely on empirical structures derived from site-scale observations, often driven solely by hydrothermal factors. However, it is critical to incorporate ecosystem-scale information for more accurate large-scale TER modeling, such as the biotic factors (e.g., clumping index and non-photosynthetic vegetation cover) linked to ecosystem-scale vegetation structure and component, and the spatiotemporal variation factors that describing the continuous variations of land cover and phenology. These ecosystem-scale variables have not been well parameterized in existing models, because the mechanisms by which they affect TER remain unclear. To address this gap, we applied a causality constrained interpretable machine learning framework (PCMCI+, XGBoost, SHAP) to model the relationships between relevant variables and TER, and established a TER estimation model called “CIML-TER”. The CIML-TER model was trained with an integrated TER observations from two major flux networks (FLUXNET and ABCflux), and was applied to estimate global monthly TER at a spatial resolution of 0.05° during 2001-2020. Global annual TER estimated with CIML-TER ranged between 117 and 125 Pg C. Moreover, CIML-TER accurately depicted the naturally continuous spatial variations of TER, which were not well described in Fluxcom and LGS-Reco due to the limitations of using traditional discrete land cover data. The CIML-TER model revealed the underestimated contributions of some complex ecosystems (such as EBFs) to global TER in previous TER products and emphasized the need for future process models to account for ecosystem-scale variables’ effects.