Evaluating the biological pump efficiency of the Last Glacial Maximum ocean using <i>δ</i><sup>13</sup>C

Abstract

Abstract. Although both physical and biological marine changes are required to explain the 100 ppm lower atmospheric pCO2 of the Last Glacial Maximum (LGM, ∼21 ka) as compared to preindustrial (PI) times, their exact contributions are debated. Proxies of past marine carbon cycling (such as δ13C) document these changes and thus provide constraints for quantifying the drivers of long-term carbon cycle variability. This modeling study discusses the physical and biological changes in the ocean needed to simulate an LGM ocean in satisfactory agreement with proxy data, here focusing especially on δ13C. We prepared a PI and LGM equilibrium simulation using the ocean model NorESM-OC with full biogeochemistry (including the carbon isotopes δ13C and radiocarbon) and dynamic sea ice. The modeled LGM–PI differences are evaluated against a wide range of physical and biogeochemical proxy data and show agreement for key aspects of the physical ocean state within the data uncertainties. However, the lack of a simulated increase of regenerated nutrients for the LGM indicates that additional biogeochemical changes are required to simulate an LGM ocean in agreement with proxy data. In order to examine these changes, we explore the potential effects of different global mean biological pump efficiencies on the simulated marine biogeochemical tracer distributions. Through estimating which biological pump efficiency reduces LGM model–proxy biases the most, we estimate that the global mean biological pump efficiency increased from 38 % (PI) to up to 75 % (LGM). The drivers of such an increase in the biological pump efficiency may be both biological and related to circulation changes that are incompletely captured by our model – such as stronger isolation of Southern Source Water. Finally, even after considering a 75 % biological pump efficiency in the LGM ocean, a remaining model–proxy error in δ13C exists that is 0.07 ‰ larger than the 0.19 ‰ data uncertainty. This error indicates that additional changes in ocean dynamics are needed to simulate an LGM ocean in agreement with proxy data.

Impact of Southern Ocean surface conditions on deep ocean circulation during the LGM: a model analysis

“…S5), as inferred from the majority of the proxy data (Curry and Oppo, 2005;Böhm et al, 2015;Skinner et al, 2017;Gebbie, 2014). In contrast, Morée et al (2021) were able to simulate with the NorESM-OC model a shoaled and slightly weaker AMOC at the LGM compared to their PI state. As the radiocarbon ages simulated in southern source waters were too young compared to data, they suggested however that the ventilation at the LGM was still overestimated, possibly in relation to a too-small Antarctic sea-ice extent in their LGM simulation (their Fig.…”

“…S12, displaying a sea-ice extent of ∼ 4.94 × 10 6 km 2 in summer and ∼ 32.95 × 10 6 km 2 in winter). However, if we consider our new estimates of ∼ 10.2×10 6 and ∼ 32.9×10 6 km 2 (respectively, for the summer and winter sea-ice extent inferred from proxy data), instead of the ones presented in Roche et al (2012), the sea-ice extent simulated by Morée et al (2021) is underestimated only in summer. Therefore, additional processes might be involved to explain the weak ventilation of Southern Ocean sourced deep water at the LGM.…”

A First Intercomparison of the Simulated LGM Carbon Results Within PMIP‐Carbon: Role of the Ocean Boundary Conditions

“…In addition, an increase of alkalinity lowers atmospheric concentrations by displacing the acid‐base equilibriums of inorganic carbon in favor of (Sigman et al., 2010). These adjustments are typically done by assuming a −3% decrease in total ocean volume (Brovkin et al., 2007), or a decrease close to this value (Bouttes et al., 2010; Morée et al., 2021). However, it should be noted that these adjustments are meant to account for the sea level change at a global scale, and do not reflect local processes such as corals or shelf erosion (Broecker, 1982).…”

“…They also encompass changes of Southern Ocean sea ice (Marzocchi & Jansen, 2019; Stephens & Keeling, 2000), ocean stratification (Francois et al., 1997), and circulation (Aldama‐Campino et al., 2020; Menviel et al., 2017; Ödalen et al., 2018; Schmittner & Galbraith, 2008; Skinner, 2009; Watson et al., 2015). Biogeochemical processes rely on changes in the cycle (Brovkin et al., 2007, 2012; Kobayashi & Oka, 2018; Matsumoto & Sarmiento, 2002) or an increased efficiency of the biological pump (Morée et al., 2021), through increased iron inputs from aeolian dust for example (Bopp et al., 2003; Oka et al., 2011; Tagliabue et al., 2009, 2014; Yamamoto et al., 2019).…”

“…As a result, it is hardly surprising that models struggle to simulate the LGM drawdown, especially in their standard version. Previous studies show that models simulate a large range of drawdown, with most modeling studies accounting for one third to two thirds of the 90100 ppm change inferred from ice core data (Brovkin et al., 2007, 2012; Buchanan et al., 2016; Hain et al., 2010; Khatiwala et al., 2019; Kobayashi & Oka, 2018; Marzocchi & Jansen, 2019; Matsumoto & Sarmiento, 2002; Morée et al., 2021; Oka et al., 2011; Stephens & Keeling, 2000; Tagliabue et al., 2009). The discrepancies between models can be partly linked to resolution (Gottschalk et al., 2020) and representation of ocean and atmosphere physics, completeness of the carbon cycle model (including sediments, permafrost…) (Kohfeld & Ridgwell, 2009), and simulated climate and ocean circulation (Menviel et al., 2017; Ödalen et al., 2018).…”