Conference Agenda

We present new estimates of primary production and net community production for the European Arctic. Primary production estimates were computed using a regional algorithm that showed higher accuracy in the Greenland Sea compared to previous studies. This improvement was achieved by integrating multiple sources of local data collected during expeditions of the Institute of Oceanology of the Polish Academy of Sciences (2015–2022), as well as campaigns of the Norwegian Polar Institute. The algorithm accounts for the local vertical distribution of chlorophyll and local particulate absorption spectrum, which significantly enhanced algorithm performance. Using this approach, we generated a time series of phytoplankton seasonal cycles for 1998–2022, revealing a more prolonged bloom period than previously reported. Our calculations indicate that total phytoplankton production is 11–150% higher than earlier estimates, implying stronger CO₂ uptake in this sector of the Arctic Ocean. The higher values primarily result from including the subsurface chlorophyll maximum, which is underrepresented in satellite observations and often omitted in models. Moreover, the use of level two satellite products extended coverage into high-latitude regions, yielding estimates in areas that level three products previously reported as zero. In parallel we present the latest advances of this work by estimating net community production from BGC-Argo float observations (2012-2024), which provides an independent constraint on regional carbon fluxes.

The ocean’s biological gravitational pump (BGP) –a set of food-web processes that generate organic particles that gravitationally sink from the surface to the deep ocean– contributes to locking away atmospheric CO₂. Despite its importance for the carbon cycle and climate, the BGP remains poorly constrained by observations owing to the ocean’s vastness, strong spatiotemporal variability, and the high cost of particle measurements. Moreover, current biogeochemical models used in climate simulations lack a process-based, mechanistic representation of the complex, food-web interactions driving the BGP, instead reducing them to a few globally uniform parameters. As a result, their capacity to capture environmental responses and realistically project future changes in the BGP is limited.

We present a novel mechanistic model, the Stochastic Lagrangian Aggregate Model of Sinking particles, version 2 (SLAMS-2.0), which explicitly simulates and tracks the formation, interactions and transformations of large numbers of biologically-produced particles within the BGP. The model is forced by satellite and hydrographic climatologies of surface ocean carbon and depth-resolved biogeochemical variables, and validated against multi-tracer particle flux observations, particle number concentrations, and particle size distributions from six contrasting time-series sites. Unlike existing biogeochemical models, SLAMS-2.0 produces fundamental BGP characteristics –such as the transfer efficiency of particulate organic carbon flux– as emergent properties rather than fixed parameterisations. Here, we will outline the architecture of SLAMS-2.0, present preliminary results from a global-ocean simulation, and discuss its potential for improving understanding of the BGP in the today’s climate and its response to future change.

Shelf seas are important for carbon sequestration and carbon cycle, but shelf sea observations for carbon pools are often sparse, or highly uncertain. Alternative can be provided by reanalyses, but these are often expensive to run. We propose to use an ensemble of neural networks (i.e. deep ensemble) to learn from a coupled physics-biogeochemistry model the relationship between the directly observable variables and variety of carbon pools (detritus, DOC, zooplankton, heterotrophic bacteria and DIC). We demonstrate for North-West European Shelf (NWES) sea environment, that when the deep ensemble trained on a model free run simulation is applied to the NWES reanalysis, it is capable to reproduce the reanalysis outputs for carbon pools and additionally provide uncertainty information. We focus on explainability of the results and discuss potential use of the deep ensembles for future climate what-if scenarios. We suggest that model-informed machine learning presents a viable alternative to expensive reanalyses, or existing satellite algorithms, so it could complement observations wherever they are missing and/or highly uncertain.

Understanding ocean carbon cycling and its sensitivity to climate change requires integrating diverse observations to resolve the roles of multiple carbon pumps, including the biological and microbial carbon pumps (BCP and MCP, respectively). While BCP exports organic carbon from surface waters to the deep ocean via sinking particles (biological gravitational pump) and actively mediated transport driven by physical and biological processes (physical and migration pumps), the MCP transforms labile dissolved organic carbon into refractory forms, contributing to long-term carbon storage. Despite their shared roles in regulating carbon fluxes, the coupling between these two pumps remains poorly understood, particularly in most oligotrophic areas, the subtropical gyres.

This study is conducted under the European Space Agency’s Ocean Carbon pillar, within the framework of the “Satellite-based observations of Carbon in the Ocean: Pools, fluxes and Exchanges” (SCOPE) project, which aims to improve observation-based estimates of carbon pools and fluxes and to support the development of satellite products for ocean carbon cycling. Focusing on the core North Atlantic Subtropical Gyre (NASTG), we investigate the coupling between BCP and MCP by integrating satellite Ocean Color observations, in situ BioGeoChemical-Argo (BGC-Argo) float profiles, and 4D observation-based reconstructions. To this aim, we compare BCP efficiency derived from satellite-based export production (EP) and primary production (PP)—SCOPE products—with instantaneous particulate organic carbon (POC) fluxes from BGC-Argo profiles. We also assess the contributions of different BCP pathways, —particularly the physical injection pump, to their coupling with the MCP. We find a significant correlation between the downward export of particles from the BCP in the productive layer and the intensity of the MCP, with a discernible half-month time lag between the two processes. This synergic approach helps to “connect the puzzle” of carbon export and transformation in one of the ocean’s most nutrient-poor regions, offering new insights into the biogeochemical functioning of the NASTG .