Conference Agenda

The ocean absorbs ~25% of anthropogenic CO₂ emissions annually, mediated by physio-chemical and biological processes. While physical drivers of oceanic CO₂ uptake are relatively well characterized, biological contributions remain poorly constrained. Advancing our understanding of biological controls is essential for monitoring and predicting climate-driven changes in the ocean carbon cycle. Therefore, we first propose a new global ocean ecological biome delineation based on ocean colour remote sensing. Next, we examine CO₂ dynamics in a temperate shelf sea at high spatial and temporal resolution.

Ecological biomes, i.e. regions of coherent biological and biogeochemical structure, have proven essential for carbon cycle studies. Yet, existing classifications rely heavily on physical variables (e.g. sea surface temperature, SST) with limited biological representation. We present a biologically-informed segmentation at 0.25° resolution based on 26 years of satellite ocean color data (ESA Ocean Colour Climate Change Initiative, OC-CCI), spanning the open and coastal ocean. Our biomes capture key surface ocean ecosystem features, including primary productivity, particulate organic carbon, and phytoplankton community structure. Their relevance for carbon cycle research is further demonstrated through biome-scale estimates of biological modulation of the seawater partial pressure of CO₂ (pCO₂).

Secondly, we examine pCO₂ dynamics within selected biome regions in the North Sea over the past decade at unprecedented (1km daily) resolution using in-situ pCO₂ observations (Surface Ocean CO₂ Atlas, SOCAT) and satellite observations of i.a. ocean colour (OC-CCI) and SST. By applying regionally-optimized retrieval algorithms, we estimate key biogeochemical drivers of pCO₂, including chlorophyll-a, suspended particulate matter and particulate organic carbon. We identify distinct biogeochemical regions shaped by primary productivity, riverine inputs, and sediment dynamics, with varying impacts on pCO₂ dynamics, from locally enhancing the CO₂ uptake to degassing CO₂. This study provides new insights into coastal carbon dynamics applicable to coastal regions globally.

Coastal regions are highly dynamic environments, where physical, chemical, and biological interactions regulate carbon exchange between the ocean and the atmosphere. Extreme warming events, such as marine heatwaves (MHWs), can strongly disrupt these fluxes, yet their fine-scale, short-term impacts on the full carbonate system remain poorly understood. The Belgian Part of the North Sea (BPNS), with its extensive in-situ observations of key carbonate system variables, provides an ideal setting to study these effects.

In this study, we combine in-situ carbonate system observations from the Surface Ocean CO₂ Atlas (SOCAT) and the Integrated Carbon Observation System (ICOS) with machine learning to reconstruct daily, 1 km-resolution maps of sea surface partial pressure of CO₂ (pCO₂, 2000–2024) and dissolved inorganic carbon (DIC, 2017–2024). Using CO2SYS, we derive pH and total alkalinity, completing the full carbonate system and enabling high-resolution assessment of air–sea CO₂ fluxes (FCO₂) during MHWs.

From 2000–2024, over 100 MHW events were detected in the BPNS using Hobday et al.’s (2016) criteria, with a 90% threshold, minimum five-day duration, up to two-day gaps, and the 1983–2012 SST climatology (daily ESA SST CCI and C3S data at 0.05°, interpolated to 1 km). On average, MHWs lasted two weeks, reached ~0.29 °C above the 90th percentile, and affected ~19% of the region. FCO₂ anomalies during MHWs, expressed as a percentage relative to the climatological FCO₂, represent deviations in CO₂ flux: positive anomalies indicate increased outgassing or reduced uptake, negative anomalies enhanced uptake or reduced outgassing. Preliminary analyses show average flux anomalies of 15 ± 13% (5%-trimmed mean), with short (<14 days) and long (≥14 days) events producing similar effects (~20% vs. ~16%). FCO₂ anomalies are primarily driven by sea surface pCO₂ changes, with limited influence from wind variability, highlighting the need for sustained CO₂ monitoring to understand coastal carbon responses under climate change.

Marine heatwaves are periods of anomalous sea surface temperatures (SST) sustained for long periods. These heatwaves have wide ranging impacts on marine ecosystems and biodiversity but can also alter the marine carbonate system and air-sea CO₂ exchange. The regional responses of the carbonate system and air-sea CO₂ exchange are likely to be different and vary during and after the marine heatwave. Within this work, we used a satellite observation–based approach to detect heatwaves in the SST records, alongside a well-characterised observational carbonate system dataset (OceanSODA ETHZ), to examine changes in the marine carbonate system before, during, and after five documented marine heatwaves: (1) Southern Ocean (2016), (2) Northeast Pacific (“The Blob”; 2015), (3) Western Australia (2011), (4) South Pacific (2016) and (5) Equatorial Indian Ocean (2016).

In all heatwaves, a significant reduction of dissolved inorganic carbon (DIC) was observed during the event with DIC anomalies increasing in magnitude beforehand and declining afterwards. The magnitude of these anomalies differed among the four heatwaves. Variations in DIC and SST appeared to drive anomalies in the fugacity of CO₂ (fCO_{2 (sw)}) and pH, due to their influence on carbonate chemistry. The impact of these heatwaves on the air-sea exchange of CO₂ varied during the heatwaves lifetime and the region and was driven by a combination of the carbonate system state, thermodynamics, and meteorological condition. We identified that the strongest anomalies in the carbonate system, and air-sea CO₂ exchange did not always coincide with the heatwave period but could occur prior to or after the heatwave. These results therefore support a need to consider the temporal sequence of any compounding events and their feedbacks.

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 .

The biodiverse and rapidly changing Greater Cape Floristic Region (GCFR) of southern Africa is outlined by coastal bays that receive dissolved organic matter (DOM) from rivers draining complex catchments composed of natural, agricultural, and urban land classes. As part of NASA's BioSCape field campaign (October-November 2023), we characterized the optical properties of three GCFR coastal bays (St. Helena, Walker, and Algoa) and four inland systems (Rietvlei wetland, Zeekoevlei lake, Theewaterskloof dam, and Klein River Estuary) in relation to carbon cycling. Measurements of the optical properties of colored DOM (CDOM), including absorption at 300 nm (ag300), spectral slope (S275-295), and fluorescence, highlighted the bio-optical complexity associated with terrestrial influences from contrasting watersheds, urban disturbances, biological activity, and rapid transformations along this dynamic coastline. CDOM in the coastal bays was characterized by an order of magnitude lower ag300 (0.5 – 2.8 m-1) and considerably higher S275-295 (0.020 – 0.031 nm-1) compared to upstream waters (25 – 71 m-1 and 0.012 – 0.019 nm-1, respectively), suggesting intense biological production and/or photochemical degradation. Coastal DOM was mostly (>75%) composed of protein-like fluorescent products, indicative of bacterial utilization, while inland DOM was mostly composed of humic and highly aromatic materials. Satellite retrievals, using OLCI and MSI imagery, captured the disproportionate yet ephemeral impact of extreme events – intense riverine discharge following extensive periods of drought – on coastal CDOM plumes, and revealed that seasonal hydrological cycles, catchment biodiversity, and human activity are the primary drivers of biogeochemical variability along this globally significant, coastal biodiversity hotspot.

The 2016 El Niño is the strongest warm ENSO phase of the 21st century recorded to date, leading to the development of severe temperature anomalies - marine heatwaves (MHWs) - across the equatorial Pacific Ocean. Here we analyze biogeochemical model output and machine learning (ML)-based reconstructions of Argo oxygen and backscattering measurements to demonstrate the impact of the 2016 El Niño in weakening oceanic carbon export - the transfer of biogenic carbon from the surface ocean to depth. We show that equatorial Pacific anomalies in modeled carbon flux, and ML-based optical particle backscatter and ecosystem respiration, display interannual oscillations linked to ENSO cycles, with an acute reduction in export (- 50 %), respiration (- 30 %) and backscatter (- 20% ), during the 2016 El Niño. This plunge in export production is attributed to a large decrease in chlorophyll biomass observed from space, and modeled ecological changes in phytoplankton community composition.

The Southern Ocean plays a vital role in global CO₂ uptake, but the magnitude and even the sign of the flux remain uncertain. Physical mechanisms for carbon uptake are emphasized, with the role of biology in surface ocean carbon uptake potentially underestimated in coastal high latitude systems, and the influence of phytoplankton phenology is underexplored. This study focuses on the West Antarctic Peninsula, a case study region experiencing rapid climate change, to examine shifts in seasonal surface ocean carbon dioxide uptake. We used 20 years of in situ air‐sea CO₂ flux data from research vessels and satellite‐derived Chlorophyll‐a data from OC-CCI as a proxy for phytoplankton biomass. OC-CCI was used for the Chlorophyll-a record because the OC-CCI atmospheric correction approach (POLYMER) results in more representative spatial distributions of coastal phytoplankton biomass than other approaches due to adjacency effects along bright icy coastlines in polar regions. We observed that the seasonal cycles of both air‐sea CO₂ flux and Chlorophyll‐a concentration intensified poleward. The amplitude of the seasonal cycle of the non‐thermal component of surface ocean pCO₂ increased with increasing latitude, while the amplitude of the thermal component remained relatively stable. These results suggest that pronounced biological uptake occurs over the shelf in austral summer despite reduced CO₂ solubility in warmer waters, which typically limits carbon uptake through physical processes. Chlorophyll-a concentrations and air-sea CO₂ fluxes were tightly coupled across all years, especially when phytoplankton biomass was high. These results suggest that an ocean-color-based air-sea CO₂ flux algorithm may be developed to estimate surface ocean carbon uptake from space.

A major challenge in understanding the oceanic carbon cycle is estimating the sinking flux of organic carbon exiting the sunlit surface ocean, i.e., carbon export. Existing algorithms derive carbon export from satellite ocean color, but often display poor accuracy. One reason is that they neglect offsets created temporally by the lag between production and export, which combined with horizontal advection can result in a spatial offset of hundreds of kilometers in dynamic regions such as Eastern Boundary Upwelling Systems. Here we use a Lagrangian “growth-advection” (GA) satellite-derived product, where plankton succession and export are mapped onto surface oceanic circulation following coastal upwelling, thus explicitly representing these offsets. We show that the GA product succeeds in representing export measured off the California coast, despite relying exclusively on satellite winds and currents (no ocean color). In situ export is best represented by a combination of GA export, proportional to modeled zooplankton, and export derived from ocean color, related to local phytoplankton. Both products also correlate with a long-term time series of abyssal carbon flux. However, their spatial and temporal patterns are very different, underscoring the need to better constrain, and take into account, zooplankton contribution to export and its offset from primary production. These results provide insights on export spatiotemporal patterns and a path toward improving satellite-derived carbon export in the California Current and beyond.

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.