Assessing the uncertainty in simulated NPP and carbon pools using four historical climate datasets
Terrestrial carbon cycle plays an important role in regulating the climate change. Accurate estimates of carbon cycle components are important for better understanding the role of terrestrial ecosystems in climate-carbon cycle feedback. Numerous studies have been conducted on the impact of climate on terrestrial carbon cycle, the uncertainties associated with knowledge of climate system, ecosystems, and their coupled interactions. Our limited understanding of the feedback mechanism between climate and ecosystems, and their inadequate representation in ecosystem models still warrants studies that capture these complexities. In this study, we are using a combination of observations, model and integrated analysis to better understand the NPP, wood carbon, root carbon and soil carbon variation across time and space, and the associated uncertainties for different observation-based historical climate conditions. We initialized the Vegetation-Global-Atmosphere-Soil (VEGAS) model with four climate data sets (PGMFD, GSWP3, WATCH, and WFDEI.GPCC) obtained from the Intersectoral Impact Model Intercomparison Project-Phase 2 (ISI-MIP2). These historical data sets represent plausible reconstruction of the climate of the past ~100 years.
The results of this study allowed us to explicitly quantify the temporal and spatial variations of NPP, wood carbon, root carbon and soil carbon under different historical climate datasets. Each data set showed its own strengths and weaknesses in terms of producing simulated results. The wood carbon and root carbon showed more sensitivity to climate data compared to NPP and soil carbon. The results partly revealed the underlying models processes causing uncertainties under different climate conditions. Overall, the findings of this study are helpful to improve our knowledge of climate-carbon cycle feedbacks through better understanding the potential uncertainties in carbon cycle. This study has wider application for further model improvement through addressing the underlying key processes that contribute to carbon dynamics uncertainties under different climate conditions.