Conference Agenda

The strong control that carbon dioxide (CO₂) emissions have over Earth's climate requires their accurate quantification and study. The ocean annually absorbs more than a quarter of all CO₂ emissions and observation-based estimates of this ocean carbon uptake (sink) have now become a key component within annual global carbon budget assessments used to guide policy. And these ocean observations form one of only two, key observational pillars and constraints within annual carbon assessments, and their uncertainties directly impact the closure of the total budget. These assessments are fairly unique in the area of Essential Climate Variables (ECVs), as data synergy approaches are fundamentally required in their generation, whereas most other ECVs require only single parameters or measurements. These ocean assessments rely heavily on multiple satellite, in situ and re-analysis datasets, but uncertainties and errors within these datasets are often unknown or poorly constrained with unknown consequences. To address these issues, the Ocean Carbon for Climate (OC4C) project are now developing the first ocean carbon assessment that focusses on using climate data records. The first version (for 1980 to 2024) uses five climate data records, and this has enabled the regional and global uncertainties to be comprehensively characterised. This presentation will present the new dataset along with example case studies to illustrate some of the many advances. This will include i) an example erroneous signal that appears when climate records are not used, ii) how inclusion of biological signals can reduce the uncertainties, iii) the power of community led experiments to investigate underlying signals, iv) how an enhanced ocean budget can be used to interrogate the global carbon budget and the land component within this, and v) how the dataset has already been used for the 2025 Planetary Health Check assessment – an effort that charts humanity’s impact on the Earth System to guide government policy.

Rapid Arctic warming and permafrost thaw are mobilizing large pools of terrestrial organic carbon (OC) into rivers, deltas, and coastal seas. Quantifying these land–ocean fluxes is essential for constraining regional and global carbon budgets, predicting carbon cycle feedbacks, and assessing impacts on vulnerable Arctic shelf ecosystems. Yet major uncertainties remain: most satellite retrievals were designed for large water bodies, while Arctic rivers and nearshore zones are characterized by narrow channels, strong salinity and optical gradients, low sun angles, and adjacency effects that challenge algorithm performance. Here, we assess the potential of optical remote sensing to trace organic carbon across Arctic land–ocean compartments. Building on high-frequency river monitoring and multi-platform campaigns in the Mackenzie–Beaufort region, we evaluate Sentinel-2 MSI and Sentinel-3 OLCI algorithms, including atmospheric correction approaches (Acolite, Polymer, C2RCC) and inherent optical property retrieval schemes (band ratios, semi-analytical, neural networks). Results show that while absolute reflectances differ across atmospheric corrections, consistent spectral shapes and newly derived bio-optical relationships enable robust retrieval of dissolved and particulate OC across riverine, deltaic, and shelf waters. Remote sensing captures the strong seasonality of Arctic rivers, including spring freshet and rain-driven pulses, and reveals the lateral spread and variability of river plumes across the shelf. Our findings demonstrate that remote sensing can capture both the seasonal dynamics of OC export and the spatial heterogeneity of carbon pathways across compartments and salinity gradients. Comparisons with in situ data reveal limitations of ocean colour algorithms, guiding their applicability in Arctic environments. Satellite monitoring offers overlooked potential in Arctic rivers and coastal transition zones, providing synoptic coverage that complements sparse and costly field data. Through regional validation, performance assessment, and application of the most reliable combinations of atmospheric correction and IOP retrieval algorithms, we demonstrate how carbon remote sensing from space can reduce uncertainties in Arctic land–ocean flux estimates.

Quantifying the ocean carbon budget and understanding how it is responding to anthropogenic forcing is a major goal in climate research. It is widely accepted that the ocean has absorbed around a quarter of CO₂ emissions released anthropogenically, and that the ocean uptake of carbon has increased in proportion to increasing CO₂ emissions. Yet, our understanding of the pools of carbon in the ocean, the processes that modulate them, and how they interact with the land and atmosphere, is not satisfactory enough to make confident predictions of how the ocean carbon budget is changing. Improving our understanding requires a holistic and integrated approach to ocean carbon cycle research, with monitoring systems capable of filling the gaps in our understanding. Satellite observations can play a major role in this. The ESA-funded ‘Satellite-based observations of Carbon in the Ocean: Pools, fluxes and Exchanges’ (SCOPE) project aims to provide the best possible characterisation of the ocean carbon budget from satellite observations and further the understanding of its variability in space and time. Here, we present the development of an internally consistent dataset of the carbon pools, fluxes and exchanges that are observable from space, including dissolved inorganic and organic carbon, particulate inorganic and organic carbon, phytoplankton carbon, primary and export production, and air-sea CO₂ and land-sea exchange. This satellite-based ocean carbon dataset is harmonised in space and time, based on climate-quality input data from the ESA’s Climate Change Initiative and fully error-characterised. This allows us, for the first time, to address both the physico-chemical and biological processes that drive the ocean carbon cycle in a consistent manner. We use the newly developed satellite-based ocean carbon dataset to analyse trends in each component of the ocean carbon cycle since the start of the ocean-colour data record in 1997. This will provide insight into how satellite observations can aid in the assessment of the ocean carbon budget in a climate context and provide useful information to evaluate and improve climate models.

Satellite-derived models of Primary Production (PP) is a critical tool to constrain the global carbon system and to better understand the mechanism of marine ecosystems with with unprecedented spatial resolution and coverage. While arguably one of the most significant developments in biological oceanography over the last 30 years, there is still questions about what the models predict (the output lies somewhere between Net and Gross PP) or how to validate the models with in situ observations. PP is furthermore only a part of the cycling of carbon in the surface ocean. To address these challenges, we have developed purely data-driven models to better constrain upper-ocean fluxes including export production (EP).

We use decision-tree models to predict primary and export production from satellite-derived properties. The approach is expanded by including depth as one input feature, allowing for depth-resolved predictions. Our approach allows us to generate purely data-driven global models with high skill showing that depth resolved PP and EP estimates are feasible. We are able to predict well constrained vertical relationships for PP and EP closely analogous to earlier analytical work. The models can in conjunction also estimate community respiration, remineralization attenuation (the Martin relationship), and export efficiency.