New Coupled Model Used Inversely for Reconstructing Past Terrestrial Carbon Storage from Pollen Data: Validation of Model Using Modern Data

The knowledge of potential impacts of climate change on terrestrial vegetation is crucial for understanding long-term global carbon cycles. Discrepancy in data has long existed between past carbon storage reconstructions since the Last Glacial Maximum by means of pollen, carbon isotope, and general circulation model (GCM) analysis. This may be due to the fact that these methods do not synthetically take into account significant differences in climate distribution between modern and past conditions as well as the effects of atmospheric CO2 concentration on vegetation. In this study, a new estimate of past biospheric carbon stocks is reported utilizing a new integrated ecosystem model (PCM) built on a physiological process vegetation model (BIOME4) coupled with a process-based biospheric carbon model (DEMETER). The PCM was constrained to fit pollen data to obtain realistic estimates. It was estimated that the probability distribution of climatic parameters, as simulated by BIOME4 in an inverse process, was compatible with pollen data while DEMETER successfully simulated the carbon storage values with the corresponding outputs of BIOME4. The carbon model was validated with observable global vegetation biomass and soil carbon, and the inversion scheme was tested against 1491 surface pollen spectra sample sites procured in Africa and Eurasia. Results show that this method can successfully simulate most biomes at selected pollen sites as well as demonstrate that the coefficient of determination (R) calculated between the observed and reconstructed modern climates vary from 0.83 to 0.97. Comparisons between the simulated biome-average terrestrial carbon variables with available observations also indicate a consensus: R variability of 0.96 for vegetation carbon density and 0.90 for soil carbon density. These results demonstrate the reliability and feasibility of this climate reconstruction method and its efficiency in reconstructing past terrestrial carbon storage.