Conference Agenda

Phytoplankton play a central role in the Earth System. Through the production of organic carbon, phytoplankton drive major processes in the ocean carbon cycle and form the basis of almost all life in the ocean. Phytoplankton have high biodiversity and this is complemented by their functional diversity, which recognises that different types of phytoplankton play varied roles in marine ecosystems and in biogeochemical cycles of the ocean. It is therefore fitting that the Global Climate Observing System (GCOS) has included phytoplankton in the ocean biosphere Essential Climate Variable (ECV), together with zooplankton. With many of the satellite-retrieval algorithms related to phytoplankton maturing over time, we are now in a position to produce such phytoplankton products to the ensemble of the European Space Agency (ESA) Climate Change Initiative (CCI). Here we present an overview of the ‘PHYTOplankton biomass and diversity Climate Change Initiative’ (PHYTO-CCI) project that aims to develop satellite-based data products for two ECVs identified by the GCOS: phytoplankton carbon biomass and pigment diversity. In the PHYTO-CCI project, satellite retrieval algorithms will be compared and combined using optical water classification to produce ECV products with associated uncertainty estimates. These products will be validated using both in situ and model data, followed by a comprehensive scientific assessment. The value of the new ocean biosphere ECVs will be demonstrated through their application in climate research and their relevance for supporting marine ecosystem services. The phytoplankton biomass and diversity ECVs are critical for understanding the structure and function of marine ecosystems, their role in the Earth System, and how they may be affected by global warming and other human-driven impacts.

A thorough understanding of the global carbon pools and cycle is essential to understand and predict the effects of climate change. Coastal waters play a key role in the global carbon cycle but remain poorly understood due to their optical complexity, high spatial variability, and sensitivity to climate change. Satellite remote sensing data can provide high spatial and temporal resolution for carbon monitoring at local, regional, and global scales. However, existing sensors are not optimised for dynamic coastal zones. Sentinel-2 MSI (S2) offers high spatial resolution, while Sentinel-3 OLCI (S3) provides better spectral band configuration, temporal resolution, and radiometric sensitivity, though its spatial resolution may be insufficient for highly heterogeneous coastal waters. In the ESA CoastalCarbonMapper project, we tested the applicability of both S3 and S2 for mapping carbon fractions—Total Organic Carbon (TOC), Dissolved Organic Carbon (DOC), Particulate Organic Carbon (POC), and Dissolved Inorganic Carbon (DIC)—in coastal waters. We aimed to develop and validate algorithms using in situ data and S2 and S3 imagery, addressing: (1) What are the optical proxies for different carbon fractions in coastal waters? (2) Which algorithms are most suitable for coastal carbon mapping? Bio-optical and physical water parameters were measured directly in the field, and water samples were collected to analyse carbon fractions and optically active water constituents in the laboratory. Measurements at the test sites were taken four times during the ice-free season of 2023–2024. Based on the collected data, potential optical proxies were identified, and retrieval algorithms for carbon fractions were developed and validated. The study represents a step forward in the remote sensing of coastal waters and Earth observation science. If adopted, the proposed carbon fraction products could allow for significant progress in different fields, from research to monitoring and policy making.

The Arctic Ocean and polar seas are undergoing rapid environmental change driven by global warming. Harsh climatic conditions and the logistical challenges of working in these regions have long limited direct in situ observations, leaving key biological processes poorly understood. Yet, the dynamics of carbon export are central to quantifying the ocean’s role in regulating climate. Here, we study these biological mechanisms using high-temporal resolution in situ measurements from 178 biogeochemical Argo (BGC-Argo) floats, one IAOOS (Ice Atmosphere Arctic Ocean Observing System) and 9 Ice-Tethered Profilers (ITPs) deployed across ice-free and sea ice–covered in the Arctic ocean and polar seas. We used data from autonomous platforms deployed in the central Arctic Ocean, Canadian polar and subpolar waters, the Greenland Sea, and the Norwegian Sea to better understand the phenology and mechanisms of phytoplankton blooms, the biological gravitational pump, the mixed-layer pump, and the eddy subduction pump and annual net community production. Results revealed a clear latitudinal gradient in the efficiency of the different carbon pumps, as well as distinct differences in bloom timing and magnitude between ice-covered and ice-free regions. In this research, we will test the hypothesis that there is a strong relationship emerges between bloom magnitude and carbon export to the deep ocean. Future work will require higher-spatial-resolution approaches to link phytoplankton functional types with their specific contributions to the biological carbon pump, ultimately improving predictions of carbon export in a rapidly changing Arctic.

The Northern Humboldt Current System (NHCS) is one of the world’s most productive upwelling ecosystems, yet knowledge of its phytoplankton composition and variability remains limited. To address this gap, we applied the PHYSAT methodology, originally developed by Alvain et al. (2008) to classify phytoplankton groups from satellite ocean-color data, marking its first use in an Eastern Boundary Upwelling Ecosystem. This work was supported by the long-term in situ phytoplankton monitoring program of the Peruvian Sea Institute (IMARPE).

Analysis of SeaWiFS and MODIS data (2003–2010) identified five major phytoplankton groups: diatoms, nano-eukaryotes, Synechococcus spp., Prochlorococcus spp., and coccolithophorids. Methodological adaptations included additional quality-control filtering and the adjustment of reflectance anomaly ranges to capture monthly variability, with a focus on diatoms.

Results revealed strong seasonal and spatial patterns. Diatoms dominated coastal waters year-round, peaking in austral summer and declining in winter, while nano-eukaryotes showed the opposite pattern. Offshore oligotrophic regions were characterized mainly by Synechococcus spp., and to a lesser extent Prochlorococcus spp. These trends were consistent across both 9 km and 1° spatial resolutions.

This study provides the first regional baseline of phytoplankton distribution in the NHCS, offering new perspectives on mesoscale variability and ecosystem responses to El Niño–Southern Oscillation (ENSO) events.

The North Sea and Baltic Sea are two highly productive, interconnected marginal seas in northern Europe that play a vital role in regional carbon cycling. Both are strongly influenced by inputs of terrestrial carbon but differ fundamentally in character. The Baltic Sea is a wind-driven, brackish water system that is almost completely enclosed by land and has residence times on the order of decades. In contrast, the North Sea is a tidally-driven, marine system, on the edge of the North Atlantic with residence times on the order of months. Episodic deep inflows of salty, oxygenated North Sea water penetrate the deep basins of the Baltic Sea, providing temporary oxygen supply to otherwise persistent hypoxic zones, while brackish, surface Baltic Sea water drains into parts of the North Sea carrying with it a net export of carbon. Both systems have densely populated and intensively used coastlines, exposed to climate change and ever-increasing anthropogenic pressures. To understand the net carbon uptake behaviour of the coupled system, and how this might change in response to perturbations in atmospheric and river forcing, we use a coupled hydrodynamic–biogeochemical model, together with an extensive observational dataset, to investigate dissolved inorganic (DIC) and organic carbon (DOC) pools over the past 25 years. We quantify large-scale carbon budgets, assess the turnover times of pelagic and benthic pools, and explore the drivers that shape long-term changes in carbon inventories. Results show that the North Sea is strongly influenced by Atlantic exchange and functions as a short-memory system, while the Baltic Sea retains perturbations for decades due to restricted circulation. Atmospheric CO₂ and alkalinity inputs emerge as dominant drivers of change, while eutrophication control and warming modulate seasonal and interannual variability. These insights are critical for understanding regional carbon sequestration potential and the response of coastal seas to climate change.

Understanding the carbon cycle is fundamental to climate change research, as marine ecosystems play a crucial role in regulating carbon storage. Particulate Organic Carbon (POC), the organic carbon in sinking plankton and detritus, is a key component of this cycle, transferring carbon from the ocean surface to the deep sea through biological processes. While Earth System Models (ESMs) are essential for predicting carbon cycle changes, recent studies highlight persisting uncertainties in marine carbon export, which pose major challenges for climate projections.

A major source of uncertainty in ESMs lies in their inconsistent ability to represent phytoplankton size classes (PSCs) and their distinct biogeochemical impacts. Diatoms, the dominant contributor to the large phytoplankton (or microhytoplankton) biomass, are projected to decline as the ocean warms and stratifies. Consequently, regional and global declines in net primary production and POC export are closely tied to diatom occurrence in current ESMs, reflecting the prevailing paradigm.

Here, we address PSC-related biases in the PISCESv2.0–NEMO4.0.4 model by implementing a restoring technique that constrains surface phytoplankton biomass using ESA satellite observations. The method distinguishes two functional groups—small phytoplankton (pico- and nanophytoplankton) and large phytoplankton (diatoms)—and enables us to quantify the impact of PSC biases on the global biological carbon pump during the satellite era. By identifying PSC bias patterns, their potential underlying mechanisms, and biogeochemical impacts, our approach provides new insights into the biological pump’s response to climate change.

Uncertainties in the ocean carbon budget, whether estimated from observations or models, reflect an incomplete understanding of carbon cycle processes. In the frame of the ESA-funded SCOPE project, we address these uncertainties by assimilating satellite-derived phytoplankton carbon (PhyC) into the ocean biogeochemical component of an Earth System Model. Two kinds of global simulations are performed: a control run with free-evolving biogeochemistry and a second bunch of experiments that include assimilation of PhyC observations. Both simulations apply physical data constraints to ensure consistent ocean circulation. The assimilation of PhyC improves the representation of surface biological activity and carbon fluxes, especially in regions with limited in situ observations. Validation against independent datasets shows reduced uncertainties in key biogeochemical variables. These results highlight the value of satellite-derived ocean biogeochemical observations for improving model representation of marine carbon processes and reducing uncertainty in ocean carbon budgets.

Satellite ocean color data provide a comprehensive, daily to weekly scale view of phytoplankton biomass dynamics in the global ocean, which is essential for resolving the seasonal cycle of the biological carbon pump. However, capturing the underlying dynamics requires knowledge of subsurface chlorophyll-a profiles, often demanding complex model constraints.

Here we present a novel approach to project surface chlorophyll-a data to subsurface profiles using a machine learning framework trained with Biogeochemical (BGC) Argo float observations. The method assumes a Markov process in the seasonal sequence of chlorophyll-a profiles measured by BGC Argo floats and predicts subsurface variability through a Hidden Markov Model (HMM).

We tested the system in the Norwegian Sea, where clusters of BGC Argo profiles have been available since 2013. The HMM’s observable vector includes satellite chlorophyll-a, sea surface temperature, ERA5 downward shortwave radiation, and mixed-layer depth from core Argo floats. The Root Mean Square Error (RMSE) of the reconstructed chlorophyll-a profiles varies with depth and season, with the highest errors (50–100 m) at the base of the mixed layer during the spring bloom.

Recently, the system was extended to retrieve particulate organic carbon (POC) and dissolved inorganic carbon (DIC) profiles, providing a more consistent representation of the seasonal cycle of the biological carbon pump in the Norwegian Sea.

The temperature dependence of phytoplankton photosynthesis and growth has been widely incorporated into satellite algorithms of primary production and global biogeochemical models. The culture study of Eppley (1972) showed that marine microalgae achieved their maximum growth rates at temperatures close to those at which the cells were initially collected, underscoring the importance of temperature as a factor governing the ecophysiology of phytoplankton cells.

The parameters of the photosynthesis-irradiance (P-E) response curves account for variability in the two primary determinants governing carbon fixation in the natural environment: the amount of biomass present and light availability. Thus, the P-E parameters serve as valuable indicators of photosynthetic efficiency.

Temperature is believed to impact the photosynthetic characteristics of phytoplankton cells through its effect on enzyme kinetics. Temperature also has a role in setting the density structure of the surface lit layer, thereby influencing the secondary determinants of algal growth, such as the supply of nutrients and light history. These factors, in turn, govern the seasonal succession of phytoplankton taxa.

Using a multi-year dataset of P-E response curves, we investigate how photosynthetic performance of natural phytoplankton assemblages varies across a wide range of temperatures in the western North Atlantic. We will also explore how information on phytoplankton community structure may serve as a useful indicator of photosynthetic efficiency, based on the premise that the forcing variables governing phytoplankton diversity also regulate the secondary determinants known to limit rates of carbon fixation.

The gradual change in meteorological regimes associated with Climate Change is rapidly modifying land ecosystems. In several parts of the world, vegetated landscapes are becoming drier and hotter, hence more prone to burn. These changing trends in global fire activity influence the global carbon budget, sometimes in non-obvious ways.

Large wildfires have the potential to perturb ocean biogeochemical balance through aerosols deposition. The deposition of wildfire' aerosols has been associated with the exceptional occurrence of phytoplankton fertilisation events, harmful algal blooms, changes in phytoplankton community composition and phenology, and anomalous bacterial activity. Understanding these relationships is critical to better constrain the net effect of wildfires in the global carbon budget. However, the casual links between wildfire activity and marine biogeochemical responses are complex and they require interdisciplinary efforts as they depend on the chemical composition at source, the aerosol's lifetime and the biogeochemical state of the receptor waters.

The ESA-funded project PYROMAR puts the focus on this increasingly relevant component of the global carbon cycle. The project brings together experts on ocean colour and aerosols satellite data, fire dynamics, sea-ice, atmospheric chemistry and ocean biogeochemistry with three objectives: (1) build an inventory of ocean biogeochemical responses to wildfire aerosols, (2) understand the limiting factors controlling the response, and (3) identify long-term trends in the coupling between fire-prone and marine ecosystems. PYROMAR will target these objectives in three regional sites: the Arctic Ocean, the Californian upwelling and the South Atlantic.

This poster will present the different tasks and tools deployed in PYROMAR's strategy as well as its complementarity with ESA-EO clusters and on-going projects.

A fundamental building block in marine primary production models is the set of model parameters essential to compute underwater light penetration and photosynthesis. A variety of models are available for satellite-based computations of primary production, which may be classified as available-light models, absorbed-light models, and growth-rate models. Regardless of the choice of the model, a set of four parameters would enable the implementation of any of them, and allow interchange of modes in a consistent manner (Sathyendranath et al. 2009). They are the initial slope of the photosynthesis-irradiance curve, the light-saturation parameter, the specific absorption of phytoplankton, and the carbon-to-chlorophyll ratio. In models where primary production is computed for multiple functional types, these parameters have to be assigned for each of them. Often, the parameters are assigned based on culture experiments. Though desirable, if only for comparison with laboratory experiments, it is not easy to obtain field data on these parameters, since, typically, phytoplankton rarely occur as single-species populations in the field. A notable exception is the work of Uitz et al. (2008, 2010), in which they estimated photosynthetic parameters from field data for three size classes, and then computed size-class-specific primary production using satellite data.

In this work, we have used a large dataset of photosynthesis-irradiance parameters from various parts of the global ocean, combined with HPLC data on pigment composition, to identify samples dominated by a single phytoplankton type. The data, segregated by dominant phytoplankton type, are then analysed to estimate mean photosynthesis parameters and other bio-optical properties, including spectral specific absorption coefficient for each phytoplankton type. The results will be presented and compared with other published results.