Quantifying biological carbon pump pathways with a data-constrained mechanistic model ensemble approach

The ability to constrain the mechanisms that transport organic carbon into the deep ocean is complicated by the multiple physical, chemical, and ecological processes that intersect to create, transform, and transport particles in the ocean. In this manuscript we develop and parameterize a data-assimilative model of the multiple pathways of the biological carbon pump (NEMUROBCP). The mechanistic model is designed to represent sinking particle flux, active transport by vertically migrating zooplankton, and passive transport by subduction and vertical mixing, while also explicitly representing multiple biological and chemical properties measured directly in the field (including nutrients, phytoplankton and zooplankton taxa, carbon dioxide and oxygen, nitrogen isotopes, and 234Thorium). Using 30 different data types (including standing stock and rate measurements related to nutrients, phytoplankton, zooplankton, and non-living organic matter) from Lagrangian experiments conducted on 11 cruises from four ocean regions, we conduct an objective statistical parameterization of the model and generate one million different potential parameter sets that are used for ensemble model simulations. The model simulates in situ parameters that were assimilated (net primary production and gravitational particle flux) and parameters that were withheld (234Thorium and nitrogen isotopes) with reasonable accuracy. Model results show that gravitational flux of sinking particles and vertical mixing of organic matter from the surface ocean are more important biological pump pathways than active transport by vertically-migrating zooplankton. However, these processes are regionally variable, with sinking particles most important in oligotrophic areas of the Gulf of Mexico and California, sinking particles and vertical mixing roughly equivalent in productive regions of the CCE and the subtropical front in the Southern Ocean, and active transport an important contributor in the Eastern Tropical Pacific. We further find that mortality at depth is an important component of active transport when mesozooplankton biomasses are high, but that it is negligible in regions with low mesozooplankton biomass. Our results also highlight the high degree of uncertainty, particularly amongst mesozooplankton functional groups, that is derived from uncertainty in model parameters, with important implications from results that rely on non-ensemble model outputs. We also discuss the implications of our results for other data assimilation approaches.

Increasing Hydrostatic Pressure Impacts the Prokaryotic Diversity during Emiliania huxleyi Aggregates Degradation

In the dark ocean, the balance between the heterotrophic carbon demand and the supply of sinking carbon through the biological carbon pump remains poorly constrained. In situ tracking of the dynamics of microbial degradation processes occurring on the gravitational sinking particles is still challenging. Our particle sinking simulator system (PASS) intends to mimic as closely as possible the in situ variations in pressure and temperature experienced by gravitational sinking particles. Here, we used the PASS to simultaneously track geochemical and microbial changes that occurred during the sinking through the mesopelagic zone of laboratory-grown Emiliania huxleyi aggregates amended by a natural microbial community sampled at 105 m depth in the North Atlantic Ocean. The impact of pressure on the prokaryotic degradation of POC and dissolution of E. huxleyi-derived calcite was not marked compared to atmospheric pressure. In contrast, using global O2 consumption monitored in real-time inside the high-pressure bottles using planar optodes via a sapphire window, a reduction of respiration rate was recorded in surface-originated community assemblages under increasing pressure conditions. Moreover, using a 16S rRNA metabarcoding survey, we demonstrated a drastic difference in transcriptionally active prokaryotes associated with particles, incubated either at atmospheric pressure or under linearly increasing hydrostatic pressure conditions. The increase in hydrostatic pressure reduced both the phylogenetic diversity and the species richness. The incubation at atmospheric pressure, however, promoted an opportunistic community of “fast” degraders from the surface (Saccharospirillaceae, Hyphomonadaceae, and Pseudoalteromonadaceae), known to be associated with surface phytoplankton blooms. In contrast, the incubation under increasing pressure condition incubations revealed an increase in the particle colonizer families Flavobacteriaceae and Rhodobacteraceae, and also Colwelliaceae, which are known to be adapted to high hydrostatic pressure. Altogether, our results underline the need to perform biodegradation experiments of particles in conditions that mimic pressure and temperature encountered during their sinking along the water column to be ecologically relevant.

Sedimentary microplankton distributions are shaped by oceanographically connected areas

Having descended through the water column, microplankton in ocean sediments are representative for the ocean surface environment, where they originated from. Sedimentary microplankton is therefore used as an archive of past and present surface oceanographic conditions. However, these particles are advected by turbulent ocean currents during their sinking journey. So far, it is unknown to what extent this particle advection shapes the microplankton composition in sediments. Here we use global simulations of sinking particles in a strongly eddying global ocean model, and define ocean bottom provinces based on the particle surface origin locations. We find that these provinces can be detected in global datasets of sedimentary microplankton assemblages, demonstrating the effect provincialism has on the composition of sedimentary remains of surface plankton. These provinces explain the microplankton composition, together with e.g. ocean surface environment. Connected provinces have implications on the optimal spatial extent of microplankton sediment sample datasets that are used for palaeoceanographic reconstructions, and on the optimal spatial averaging of sediment samples over global datasets.

Bridging the gaps between particulate backscattering measurements and modeled particulate organic carbon in the ocean

Oceanic particulate organic carbon (POC) is a relatively small (~4 Pg C) but dynamic component of the global carbon cycle with fast mean turnover rates compared to other oceanic, continental and atmospheric carbon stocks. Biogeochemical models historically focused on reproducing the sinking flux of POC driven by large fast-sinking particles (bPOC). However, suspended and slow-sinking particles (sPOC) typically represent 80–90 % of the POC stock, and can make important seasonal contributions to vertical fluxes through the mesopelagic layer (200–1000 m). Recent developments in the parameterization of POC reactivity in the PISCES model (PISCESv2_RC) have greatly improved its ability to capture sPOC dynamics. Here we evaluated this model by matching 3D and 1D simulations with BGC-Argo and satellite observations in globally representative ocean biomes, building on a refined scheme for converting particulate backscattering profiles measured by BGC-Argo floats to POC. We show that PISCES captures the major features of sPOC and bPOC as seen by BGC-Argo floats across a range of spatiotemporal scales, from highly resolved profile time series to biome-aggregated climatological profiles. Our results also illustrate how the comparison between the model and observations is hampered by (1) the uncertainties in empirical POC estimation, (2) the imperfect correspondence between modelled and observed variables, and (3) the bias between modelled and observed physics. Despite these limitations, we identified characteristic patterns of model-observations misfits in the mesopelagic layer of subpolar and subtropical gyres. These misfits likely result from both suboptimal model parameters and model equations themselves, pointing to the need to improve the model representation of processes with a critical influence on POC dynamics, such as sinking, remineralization, (dis)aggregation and zooplankton activity. Beyond model evaluation results, our analysis identified inconsistencies between current estimates of POC from satellite and BGC-Argo data, as well as POC partitioning into phytoplankton, heterotrophs and detritus deduced from in situ bio-optical data. Our approach can help constrain POC stocks, and ultimately budgets, in the epipelagic and mesopelagic ocean.