[A-CG52] Science in the Arctic Region

[ACG52-11] Changes in Terrestrial Carbon Cycle in Siberia Based on Multi Model-Data Approach

*Beak Munseon¹, Kazuhito Ichii¹, Beichen Zhang¹, Daniel Henri¹, Ruci Wang¹, Misaki Hase¹, Naveen Chandra², Prabir Patra² (1.Chiba University, 2.Japan Agency for Marine-Earth Science and Technology)

Keywords:terrestrial carbon cycle, process-based model, data-driven model, remote sensing

In the early 21st century, Siberia has been experiencing rapid climate change, characterized by rising surface temperatures and increased precipitation. Siberia warrants attention due to its extensive and diverse vegetation types, which are particularly vulnerable to climate change because of the constraints imposed by low temperatures and low precipitation. In this study, we aimed to understand interannual variations and trends in the terrestrial carbon cycle in Siberia using various models and data, including process-based models, remote sensing-based models, data-driven estimations, and atmospheric inversions. Gross Primary Productivity (GPP) and net biome productivity (NBP) are the target variables in this study. First, we compared multiple models to evaluate their consistency in estimating terrestrial carbon fluxes. Second, we identified transition years by applying a piecewise fit to detect shifts among models. Finally, we conducted a series of sensitivity tests to determine the key mechanisms driving changes in the terrestrial carbon cycle. We found that interannual variations in GPP and NEE are mostly consistent among methods, with some discrepancies between bottom-up and top-down estimations. The differences between top-down and bottom-up estimations are partially explained by emissions from biomass burning. We also confirmed that increases in GPP and NBP reached the maximum around 2010 but decreased after 2010. Furthermore, extreme weather has caused higher GPP in recent years, but there have not been many changes in NBP. These analyses provide insights into the robustness of carbon flux estimates and contribute to a better understanding of the underlying processes governing carbon dynamics in Siberia.