Abstract. We present an extension to the carbon-centric Grid Enabled Integrated Earth system model (cGEnIE) that explicitly accounts for the growth and interaction of an arbitrary number of plankton species. The new package (ECOGEM) replaces the implicit, flux-based parameterisation of the plankton community currently employed, with explicitly resolved plankton populations and ecological dynamics. In ECOGEM, any number of plankton species, with ecophysiological traits (e.g. growth and grazing rates) assigned according to organism size and functional group (e.g. phytoplankton and zooplankton) can be incorporated at runtime. We illustrate the capability of the marine ecology enabled Earth system model (EcoGEnIE) by comparing results from one configuration of ECOGEM (with eight generic phytoplankton and zooplankton size classes) to climatological and seasonal observations. We find that the new ecological components of the model show reasonable agreement with both global-scale climatological and local-scale seasonal data. We also compare EcoGEnIE results to the existing biogeochemical incarnation of cGEnIE. We find that the resulting global-scale distributions of phosphate, iron, dissolved inorganic carbon, alkalinity, and oxygen are similar for both iterations of the model. A slight deterioration in some fields in EcoGEnIE (relative to the data) is observed, although we make no attempt to re-tune the overall marine cycling of carbon and nutrients here. The increased capabilities of EcoGEnIE in this regard will enable future exploration of the ecological community on much longer timescales than have previously been examined in global ocean ecosystem models and particularly for past climates and global biogeochemical cycles.

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Multiple linear regression analysis (MLRA) applied to sediment trap data has been highly influential in identifying a plausible ‘ballasting’ mechanism that directly links the settling fluxes of particulate organic carbon (POC) to those of denser, inorganic minerals. However, analysis to date has primarily been carried out at the global scale, missing spatial variability in the flux relationships that may be important. In this paper, Geographically Weighted Regression (GWR) is applied to an updated deep (>1500 m) sediment trap database (n = 156), using the MLRA approach of Klaas and Archer (2002) but now allowing the carrying coefficients to vary in space. While the global mean carrying coefficient values for CaCO3, opal, and lithogenics are broadly consistent with previous work, the GWR analysis reveals the existence of substantial and statistically significant spatial variability in all three carrying coefficients. In particular, the absence of a strong globally uniform relationship between CaCO3 and POC in our spatial analysis calls into question whether a simple ballasting mechanism exists. Instead, the existence of coherent spatial patterns in carrying coefficients, which are reminiscent of biogeochemical provinces, points toward differences in specific pelagic ecosystem characteristics as being the likely underlying cause of the flux relationships sampled by sediment traps. Our findings present a challenge to ocean carbon cycle modelers who to date have applied a single statistical global relationship in their carbon flux parameterizations when considering mineral ballasting, and provide a further clue as to how the efficiency of the biological pump in the modern ocean is regulated.

The ocean is the biggest carbon reservoir in the surficial carbon cycle and, thus, plays a crucial role in regulating atmospheric CO 2 concentrations. Arguably, the most important single component of the oceanic carbon cycle is the biologically driven sequestration of carbon in both organic and inorganic form-the so-called biological carbon pump. Over the geological past, the intensity of the biological carbon pump has experienced important variability linked to extreme climate events and perturbations of the global carbon cycle. Over the past decades, significant progress has been made in understanding the complex process interplay that controls the intensity of the biological carbon pump. In addition, a number of different paleoclimate modelling tools have been developed and applied to quantitatively explore the biological carbon pump during past climate perturbations and its possible feedbacks on the evolution of the global climate over geological timescales. Here we provide the first, comprehensive overview of the description of the biological carbon pump in these paleoclimate models with the aim of critically evaluating their ability to represent past marine carbon cycle dynamics. First, the paper provides an overview of paleoclimate models and paleoapplications for a selection of Earth system box models and Earth system Models of Intermediate Complexity (EMICs). Secondly, the paper reviews and evaluates three key processes of the marine organic and inorganic carbon cycling and their representation in the discussed paleoclimate models: biological productivity at the ocean surface, remineralisation/dissolution of particulate carbon within the water column and the benthic-pelagic coupling at the seafloor. Illustrative examples using the model GENIE show how different parameterisations of water-column and sediment processes 1 can lead to significantly different model results. The presented compilation reveals that existing paleoclimate models tend to employ static parametrisations of the biological carbon pump that are empirically derived from present-day observations. These approaches tend to represent carbon transfer in the modern ocean well; however, their empirical nature compromises their applicability to past 25 climate events characterized by fundamentally different environmental conditions. GENIE results show that paleoclimate models may for instance over-or underestimate carbon sequestration in the ocean-sediment system with important implications for the accuracy of the predicted climate response. Finally, the paper discusses the importance of using models of different complexities and gives suggestions how they can be applied to quantify various model uncertainties.

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