Conference Agenda

Monitoring marine carbon pools and fluxes is critical for understanding the ocean's role in the global carbon cycle. Satellite ocean color data provide a unique tool for assessing key biogeochemical parameters, but their accuracy relies on robust regional algorithms. Since 2002, the Ocean Optics Laboratory at the Shirshov Institute of Oceanology has developed an electronic Atlas for the Russian seas based on satellite data and validated regional algorithms (http://optics.ocean.ru). The Atlas offers enhanced accuracy for bio-optical characteristics, which are fundamental proxies for quantifying carbon-related processes, primarily chlorophyll-a and coccolithophore concentrations. This study leverages over than two decades (1998–2024) of satellite observations processed with these regional algorithms to analyze interannual variability and trends in key parameters linked to the ocean carbon cycle in the Barents, Kara, Laptev, White, Baltic, Black, and Caspian seas. We present analyzed trends in these parameters, which provide valuable insights for future carbon cycle studies. Notably, a significant positive trend in coccolithophore concentration in the northeastern Black Sea confirms the intensification of blooms, which has implications for calcium carbonate production and export fluxes. Similarly, a positive trend in chlorophyll-a concentration in the northern Barents Sea, likely associated with regional sea ice decline, points to increased primary production and potential carbon drawdown. Such long-term observations are crucial for validating and improving climate models that represent high-latitude and marginal seas.

Marine net primary production is a cornerstone of the global carbon cycle and a foundation of marine ecosystems. As one of the largest carbon fluxes on the planet, it sustains marine biodiversity, underpins global ocean ecosystems and supports critical ecosystem services. Climate change is disrupting marine primary production in complex and poorly understood ways, with major implications for the carbon cycle, food security and climate feedbacks. Yet there remains little consensus on either the direction or magnitude of projected change. To address this uncertainty, we analyse remote sensing net primary production trends using six different algorithms, and benchmark them against fifteen divergent model projections. Our results suggest that future declines in production are more likely than current models predict, and that even the best-performing models still underestimate the magnitude of ongoing declines. However, large uncertainties remain, as trend estimates and model rankings depend strongly on the choice of remote sensing algorithm. To address this knowledge gap, we applied a subset of these algorithms to biogeochemical-Argo measurements. Although this offers less spatial and temporal coverage than satellites, it provides critical information at depth. Most notably, the disagreement in trend direction across ocean biomes disappears in this framework, and estimated changes are often much larger than those inferred from remote sensing alone. Together, these findings highlight both the promise and the limitations of current approaches to quantifying ocean productivity and its role in the carbon cycle. They underscore the urgent need for an integrated strategy that brings together satellite observations, autonomous platforms and biogeochemical models. Such integration is essential not only to constrain projections, but also to generate new mechanistic understanding of the drivers of ocean productivity change, knowledge critical for improving models and informing climate policy.

Responses of the Earth system to climate change may not be gradual, but abrupt in the form of tipping points. A new ESA project ‘TIME’ on Tipping points and abrupt changes In Marine Ecosystems, is designed to investigate eight vulnerable elements of the marine ecosystems for evidence of loss of resilience or for signs of abrupt changes, based on satellite data. For one of these elements: coccolithophores, a type of phytoplankton covered with highly reflective calcium plates, is unique in its ability to be studied globally even using coarse visible satellite data.

Previous studies have shown that blooms of coccolithophores are starting to appear in new areas such as sub-polar waters, and are becoming more prominent at mid-latitudes. In TIME we have extended our dataset to generate a consistent 45-year time series of AVHRR data for analysis of coccolithophores, which will be used to investigate the drivers of change using AI tools. In this presentation, we will present preliminary results of this unique dataset.

pim@pml.ac.uk