[A-CG39] Global Carbon Cycle Observation and Analysis

[ACG39-P15] Modeling of long-term δ¹³C variability in atmospheric CO₂ : Insights into carbon exchange processes among atmosphere, biosphere, and ocean

*UDDALAK CHAKRABORTY¹, Naoko Saitoh¹, Prabir K. Patra², Naveen Chandra², Dmitry A. Belikov¹ (1.Centre for Environmental Remote Sensing, Chiba University, 2.Japan Agency for Marine-Earth Science and Technology (JAMSTEC))

Keywords:Carbon exchange processes, ENSO influence, Atmosphere-biosphere-ocean interactions

Carbon dioxide (CO₂), the primary anthropogenic greenhouse gas (GHG), plays a significant role in global warming. The ¹³C/¹²C carbon isotopic ratio in atmospheric CO₂, as well as in the ocean and land carbon reservoirs, is determined by both anthropogenic activities and natural processes. Anthropogenic emissions alter these isotopic signatures, while natural carbon exchanges (between air, land, and ocean) do not only respond to climate change, but can also play a role in influencing it. While previous studies have examined long-term trends in δ¹³C, there remains limited understanding of how short-term climate variability, particularly the El Niño Southern Oscillation (ENSO), influences δ¹³C seasonal cycles and growth rates. Additionally, discrepancies between model simulations and observations suggest the incompleteness of current representations of biosphere and ocean carbon fluxes. In this study, we address these gaps, which are essential for improving our understanding of the responses of natural carbon sinks to anthropogenic CO₂ emissions and their role in shaping atmospheric δ¹³C variability.
Here, we use forward simulations of δ¹³C using MIROC4-ACTM (Model for Interdisciplinary Research on Climate, version 4) (Patra et al., 2018), an atmospheric general circulation model (AGCM)-based chemistry-transport model, to investigate long-term (1940-2020) changes in atmospheric CO₂ distributions and the underlying processes affecting its short - and long-term variability. The simulations incorporate fossil fuel emission from GridFED (version 2023.1) (Jones et al., 2021), terrestrial land biosphere fluxes from LENS and VISIT (NCAR, Ito et al., 2007), and ocean exchange fluxes from CESM2 and LENS (NCAR, Danabasoglu et al., 2020).
The model simulations were evaluated with observations from ten Scripps Institute of Oceanography (SIO) sites (Keeling et al., 2001) and ice-core and firn records (Rubino et al., 2013). The simulations successfully reproduced the long-term decline trend of δ¹³C, which was consistent with the Suess effect driven by fossil fuel emissions. They well captured seasonal variations with δ¹³C enrichment during photosynthetic uptake (summer) and depletion during respiration (winter). The results demonstrate a clear relationship between ENSO and variations in δ¹³C; El Niño events lead to a more pronounced depletion of δ¹³C due to enhanced terrestrial respiration and reduced biospheric uptake, while La Niña phases show opposite effects with stronger δ¹³C enrichment. Our model also captured El Niño-driven sea surface temperature (SST) anomalies that reduce CO₂ solubility, weakening oceanic uptake and further contributing to the δ¹³C decline. Conversely, during La Niña events, atmospheric δ¹³C increases due to stronger biospheric uptake and more efficient oceanic CO₂ sequestration, while enhanced upwelling and ventilation release older, ¹³C-depleted carbon from deep waters, slightly moderating the overall increase. The model simulations accurately captured the seasonal cycle characteristics of δ¹³C, showing enrichment during summer due to increased photosynthetic uptake and depletion in winter driven by enhanced respiration. The amplitude variations were well represented, aligning with ENSO-related shifts in biospheric and oceanic exchange. Additionally, the growth rate analysis of δ¹³C revealed its strong correlation with SOI variations, confirming that ENSO plays a dominant role in regulating interannual isotopic fluxes. These findings could improve our understanding of how ENSO modulates δ¹³C on a global scale and provide valuable insights into carbon-climate feedback mechanisms beyond CO₂ concentration trends alone.