Simulating stable carbon isotopes in the ocean component of the FAMOUS general circulation model with MOSES1 (XOAVI)

Abstract

Abstract. Ocean circulation and the marine carbon cycle can be indirectly inferred from stable and radiogenic carbon isotope ratios (δ13C and Δ14C, respectively), measured directly in the water column, or recorded in geological archives such as sedimentary microfossils and corals. However, interpreting these records is non-trivial because they reflect a complex interplay between physical and biogeochemical processes. By directly simulating multiple isotopic tracer fields within numerical models, we can improve our understanding of the processes that control large-scale isotope distributions and interpolate the spatiotemporal gaps in both modern and palaeo datasets. We have added the stable isotope 13C to the ocean component of the FAMOUS coupled atmosphere–ocean general circulation model, which is a valuable tool for simulating complex feedbacks between different Earth system processes on decadal to multi-millennial timescales. We tested three different biological fractionation parameterisations to account for the uncertainty associated with equilibrium fractionation during photosynthesis and used sensitivity experiments to quantify the effects of fractionation during air–sea gas exchange and primary productivity on the simulated δ13CDIC distributions. Following a 10 000-year pre-industrial spin-up, we simulated the Suess effect (the isotopic imprint of anthropogenic fossil fuel burning) to assess the performance of the model in replicating modern observations. Our implementation captures the large-scale structure and range of δ13CDIC observations in the surface ocean, but the simulated values are too high at all depths, which we infer is due to biases in the biological pump. In the first instance, the new 13C tracer will therefore be useful for recalibrating both the physical and biogeochemical components of FAMOUS.

Incorporating the stable carbon isotope <sup>13</sup>C in the ocean biogeochemical component of the Max Planck Institute Earth System Model

“…Here a higher NRMSE indicates the model captures a smaller fraction of the variation in observations. The performance of Hist_Popp regarding δ 13 C POC compares well to that of the FAMOUS model (Dentith et al, 2020;comparing their Fig. 8 and Fig.…”

“…4b, d and f). This is also seen in a recent study by Dentith et al (2020), who tested Popp p and Laws p with the FAst Met Office/UK Universities Simulator (FAMOUS). The underestimation in the global mean and in the meridional gradient of δ 13 C POC in Hist_Laws suggests that the parameters of the linear fit in Eq.…”

“…6g-i). Such interior-ocean δ 13 C DIC difference caused by using different parameterisation for biological fractionation is also seen in Jahn et al (2015) and Dentith et al (2020). The seasonal upward transport of the lower deep-ocean δ 13 C DIC in Hist_Laws leads to lower annualmean surface δ 13 C DIC and larger δ 13 C DIC annual range in regions of upwelling (Fig.…”

Optimisation of the marine Nd isotope scheme in the ocean component of the FAMOUS general circulation model

“…In brief, the atmospheric model of FAMOUS is based on quasi-hydrostatic primitive equations, has a horizontal resolution of 5° latitude FAMOUS is currently capable of simulating on the order of 650 model years per wall clock day on 16 processors. As such, it is well suited to running large ensembles (Gregoire et al, 2011) and performing sensitivity studies (Gregoire et al, 2015;Smith and Gregory, 2009), alongside multi-millennial length simulations (Gregoire et al, 2012(Gregoire et al, , 2015Dentith et al, 2020), as is done here.…”

Simulating marine neodymium isotope distributions using ND v1.0 coupled to the ocean component of the FAMOUS-MOSES1 climate model: sensitivities to reversible scavenging efficiency and benthic source distributions

“…Thus, FAMOUS achieves a current speed of up to 650 model years per wall clock day on 16 processors, making it ideal for running large ensembles (Gregoire et al, 2011(Gregoire et al, , 2016, more bespoke sensitivity studies (Smith and Gregory, 2009;Gregoire et al, 2015), and multimillennial simulations, e.g. to examine ice sheet behaviours (Gregoire et al, 2012(Gregoire et al, , 2016(Gregoire et al, , 2015, ocean drifts (Dentith et al, 2019) and biogeochemistry (Dentith et al, 2020).…”