Ocean carbon from space: Current status and priorities for the next decade

The ocean plays a central role in modulating the Earth’s carbon cycle. Monitoring how the ocean carbon cycle is changing is fundamental to managing climate change. Satellite remote sensing is currently our best tool for viewing the ocean surface globally and systematically, at high spatial and temporal resolutions, and the past few decades have seen an exponential growth in studies utilising satellite data for ocean carbon research. Satellite-based observations must be combined with in-situ observations and models, to obtain a comprehensive view of ocean carbon pools and fluxes. To help prioritise future research in this area, a workshop was organised that assembled leading experts working on the topic, from around the world, including remote-sensing scientists, field scientists and modellers, with the goal to articulate a collective view of the current status of ocean carbon research, identify gaps in knowledge, and formulate a scientific roadmap for the next decade, with an emphasis on evaluating where satellite remote sensing may contribute. A total of 449 scientists and stakeholders participated (with balanced gender representation), from North and South America, Europe, Asia, Africa, and Oceania. Sessions targeted both inorganic and organic pools of carbon in the ocean, in both dissolved and particulate form, as well as major fluxes of carbon between reservoirs (e.g., primary production) and at interfaces (e.g., air-sea and land–ocean). Extreme events, blue carbon and carbon budgeting were also key topics discussed. Emerging priorities identified include: expanding the networks and quality of in-situ observations; improved satellite retrievals; improved uncertainty quantification; improved understanding of vertical distributions; integration with models; improved techniques to bridge spatial and temporal scales of the different data sources; and improved fundamental understanding of the ocean carbon cycle, and of the interactions among pools of carbon and light. We also report on priorities for the specific pools and fluxes studied, and highlight issues and concerns that arose during discussions, such as the need to consider the environmental impact of satellites or space activities; the role satellites can play in monitoring ocean carbon dioxide removal approaches; economic valuation of the satellite based information; to consider how satellites can contribute to monitoring cycles of other important climatically-relevant compounds and elements; to promote diversity and inclusivity in ocean carbon research; to bring together communities working on different aspects of planetary carbon; maximising use of international bodies; to follow an open science approach; to explore new and innovative ways to remotely monitor ocean carbon; and to harness quantum computing. Overall, this paper provides a comprehensive scientific roadmap for the next decade on how satellite remote sensing could help monitor the ocean carbon cycle, and its links to the other domains, such as terrestrial and atmosphere

Variations of the organic matter composition in the sea surface microlayer: A comparison between open ocean and upwelling sites off the Peruvian coast.

The sea surface microlayer (SML) is the thin boundary layer between the ocean and the atmosphere, making it important for air-sea exchange processes. However, little is known about what controls organic matter composition in the SML. In particular, there are only few studies available on the differences of the SML of various oceanic systems. Here, we compared the organic matter and neuston species composition in the SML and the underlying water (ULW) at 11 stations with varying distance from the coast in the Peruvian upwelling regime, a system with high emissions of climate relevant trace gases, such as N2O and CO2. In the open ocean, organic carbon, and amino acids were highly enriched in the SML compared to the ULW. The enrichment decreased at the coastal stations and vanished in the upwelling regime. At the same time, the degradation of organic matter increased from the open ocean to the upwelling stations. This suggests that in the open ocean, upward transport processes or new production of organic matter within the SML are faster than degradation processes. Phytoplankton was generally not enriched in the SML, one group though, the Trichodesmium-like TrL (possibly containing Trichodesmium), were enriched in the open ocean but not in the upwelling region indicating that they find a favorable habitat in the open ocean SML. Our data show that the SML is a distinct habitat; its composition is more similar among different systems than between SML and ULW of a single station. Generally the enrichment of organic matter is assumed to be reduced when encountering low primary production and high wind speeds. However, our study shows the highest enrichments of organic matter in the open ocean which had the lowest primary production and the highest wind speeds

Remote sensing of coccolithophore blooms in selected oceanic regions using the PhytoDOAS method applied to hyper-spectral satellite data

In this study temporal variations of coccolithophore blooms are investigated using satellite data. Eight years (from 2003 to 2010) of data of SCIAMACHY, a hyper-spectral satellite sensor on-board ENVISAT, were processed by the PhytoDOAS method to monitor the biomass of coccolithophores in three selected regions. These regions are characterized by frequent occurrence of large coccolithophore blooms. The retrieval results, shown as monthly mean time series, were compared to related satellite products, including the total surface phytoplankton, i.e. total chlorophyll a (from GlobColour merged data) and the particulate inorganic carbon (from MODIS-Aqua). The inter-annual variations of the phytoplankton bloom cycles and their maximum monthly mean values have been compared in the three selected regions to the variations of the geophysical parameters: sea-surface temperature (SST), mixed-layer depth (MLD) and surface wind-speed, which are known to affect phytoplankton dynamics. For each region, the anomalies and linear trends of the monitored parameters over the period of this study have been computed. The patterns of total phytoplankton biomass and specific dynamics of coccolithophore chlorophyll a in the selected regions are discussed in relation to other studies. The PhytoDOAS results are consistent with the two other ocean color products and support the reported dependencies of coccolithophore biomass dynamics on the compared geophysical variables. This suggests that PhytoDOAS is a valid method for retrieving coccolithophore biomass and for monitoring its bloom developments in the global oceans. Future applications of time series studies using the PhytoDOAS data set are proposed, also using the new upcoming generations of hyper-spectral satellite sensors with improved spatial resolution

Amplified Arctic Surface Warming and Sea Ice Loss Due to Phytoplankton and Colored Dissolved Material

Optically active water constituents attenuate solar radiation and hence affect the vertical distribution of energy in the upper ocean. To understand their implications, we operate an ocean biogeochemical model coupled to a general circulation model with sea ice. Incorporating the effect of phytoplankton and colored dissolved organic matter (CDOM) on light attenuation in the model increases the sea surface temperature in summer and decreases sea ice concentration in the Arctic Ocean. Locally, the sea ice season is reduced by up to one month. CDOM drives a significant part of these changes, suggesting that an increase of this material will amplify the observed Arctic surface warming through its direct thermal effect. Indirectly, changing advective processes in the Nordic Seas may further intensify this effect. Our results emphasize the phytoplankton and CDOM feedbacks on the Arctic ocean and sea ice system and underline the need to consider these effects in future modeling studies to enhance their plausibility

SynSenPFT: ein globaler Datensatz zur Verteilung von funktionalen Gruppen von Phytoplankton im Ozean.

Eine Studie der Arbeitsgruppe „Phytooptics“ am AWI, die in Zusammenarbeit mit dem Institut für Umweltphysik der Universität Bremen (IUP-UB), dem „Laboratoire d’Océanographie de Villefranche (LOV, Villefranche, Frankreichce) und dems „Plymouth Marine Laboratory (PML, Plymouth, UKnited Kingdom) durchgeführterstellt wurde, entwickelte eine Methodeschlägt nun vor, wie man, die diese oben genannten Mängel der gegenwärtigen multispektralen PFT Produkte (Bereitstellen von entweder nur dominanten Phytoplanktongruppen oder Datenprodukte mit einer besonderen Koppelung an a priori Information, Bracher et al. 2017a?) und dervon gegenwärtigen Phyto DOAS-Datenprodukten (niedrige zeitliche und räumliche Abdeckung) beheben kann. Die Autoren kombiniertenuntersuchten eine Möglichkeit, um die hyperspektralen Daten, welche durch eine hohe spektrale und eine grobe räumliche Auflösung charakterisiert sind, mit multispektralen Daten, die eine höhere räumliche und zeitliche Auflösung besitzen, zu verknüpfen (SynSenPFT, Losa et al. 2017a?). Der SynSenPFT Algorithmus (Abbildung 1) basiert auf den ursprünglichen Eingabedaten von überarbeitetern Versionen (Bracher et al. 2017b, Losa et al. 2017a) dervon existierenden PFT Algorithmen, die auf hyper- und multispektralen Informationen basieren – PhytoDOAS (Bracher et al. 2009, Sadeghi et al. 2012, Bracher et al. 2017?) und OC-PFT (Hirata et al. 2011, Soppa et al. 2014). Durch eine synergistische Verknüpfung mittels optimaler Interpolation leitet der neue Algorithmus PFT Produkte mit zeitlicher und räumlicher Auflösung von multispektralen „ocean colour“-Daten ab, allerdings basierend auf der Nutzung der spektralen Informationen der hyperspektralen Daten. Die Grundzüge des Algorithmus, die die größte Herausforderung bei der späteren Implementierung darstellen, zusammen mit die Sensitivitätsstudien und die Evaluierung anhand eines großen globalen in-situ PFTs Chla-Datensatzes (Soppa et al. 2017), wurden in Losa et al. 2017a? veröffentlicht. In der Veröffentlichung heben die Autoren die Perspektiven des SynSenPFT-Systems für zukünftige Anwendungen hervor, bezogen auf die hyperspektralen Sensoren Sentinel-5-Precursor, Sentinel-4 und Sentinel-5 und dem multispektralen Sensor OLCI auf Sentinel-3. Ziel dabei ist die weiter verbesserte räumliche Auflösung des erhaltenen PFT Chla Produktes und die Verlängerung der Zeitserien über die nächsten Dekaden mit Hilfe der Sentinel-Missionen

Antarctic phytoplankton in response to environmental changes studied by a synergistic approach using multi- and hyper-spectral satellite data (PhySyn)

The project focuses on the assessment of the impact of environmental change in the Southern Ocean on phytoplankton. Phytoplankton is the key organism determining the functioning of the marine ecosystem and biogeochemical cycle and it can be detected from space. In this study analytical bio-optical retrieval techniques are to be used to develop generic methods, which extract unique global long-term information on phytoplankton composition. The methods will be based on using all available high-resolution optical satellite data which are complemented by in-situ and multi-spectral satellite data. Combined with modeling studies, this information will be used to attribute the relative importance of anthropogenic activity and natural phenomena on the marine ecosystem and biogeochemical cycling of the Southern Oceans during the last decades

Data needs for hyperspectral detection of algal bloom diversity across the globe.

A group of 38 experts specializing in hyperspectral remote-sensing methods for aquatic ecosystems attended an interactive Euromarine Foresight Workshop at the Flanders Marine Institute (VLIZ) in Ostend, Belgium, June 4–6, 2019. The objective of this workshop was to develop recommendations for comprehensive, efficient, and effective laboratory and field programs to supply data for development of algorithms and validation of hyperspectral satellite imagery for micro-, macro- and endosymbiotic algal characterization across the globe. The international group of researchers from Europe, Asia, Australia, and North and South America (see online Supplementary Materials) tackled how to develop global databases that merge hyperspectral optics and phytoplankton group composition to support the next generation of hyperspectral satellites for assessing biodiversity in the ocean and in food webs and for detecting water quality issues such as harmful algal blooms. Through stimulating discussions in breakout groups, the team formulated a host of diverse programmatic recommendations on topics such as how to better integrate optics into phytoplankton monitoring programs; approaches to validating phytoplankton composition with ocean color measurements and satellite imagery; new database specifications that match optical data with phytoplankton composition data; requirements for new instrumentation that can be implemented on floats, moorings, drones, and other platforms; and the development of international task forces. Because in situ observations of phytoplankton biogeography and abundance are scarce, and many vast oceanic regions are too remote to be routinely monitored, satellite observations are required to fully comprehend the diversity of micro-, macro-, and endosymbiotic algae and any variability due to climate change. Ocean color remote sensing that provides regular synoptic monitoring of aquatic ecosystems is an excellent tool for assessing biodiversity and abundance of phytoplankton and algae in aquatic ecosystems. However, neither the spatial, temporal, nor spectral resolution of the current ocean color missions are sufficient to characterize phytoplankton community composition adequately. The near-daily overpasses from ocean color satellites are useful for detecting the presence of blooms, but the spatial resolution is often too coarse to assess the patchy distribution of blooms, and the multiband spectral resolution is generally insufficient to identify different types of phytoplankton from each other, even if progress has undeniably been achieved during the last two decades (e.g., IOCGG, 2014). Moreover, the methods developed for multichannel sensor use are often highly tuned to a region but are inaccurate when applied broadly. New orbital imaging spectrometers are being developed that cover the full visible and near-infrared spectrum with a large number of narrow bands dubbed “hyperspectral” (e.g., TROPOMI, PRISMA, EnMAP, PACE, CHIME, SBG). Hyper-spectral methods have been explored for many years to assess phytoplankton groups and map seafloor habitats. However, the utility of hyperspectral imaging still needs to be demonstrated across diverse aquatic regimes. Aquatic applications of hyperspectral imagery have been limited by both the technology and the ability to validate products. Some of the past hyperspectral space-based sensors have suffered from calibration artifacts, low sensitivity in aquatic ecosystems (e.g., CHRIS, HICO), and very low spatial resolution (e.g., SCIAMACHY), but the next generation of sensors are planned to have high signal-to-noise ratio and improved performance over aquatic targets. Providing data to develop and validate hyperspectral approaches to characterize phytoplankton groups across the globe poses new challenges. Several recent studies have documented gaps that need to be filled in order to assess algal diversity across the globe (IOCCG, 2014; Mouw et al., 2015; Bracher et al., 2017), which promoted/inspired the formation of this workshop

Assessing the impact of climate change on phytoplankton in Fram Strait: 1. particle absorption properties from continuous measurements of spectral absorption attenuation sensor meter (AC-S)

The decline of ArcGc sea ice as well as its resulting feedbacks is asserted to have great impacts on the Arctic phytoplankton, and caused large regional variations in primary production range in the Arctic Ocean and its marginal seas. Understanding and quantifying such impacts are critical to appreciate the Arctic as a system and allow diagnostic modeling of its current status and dynamics. To assess the above impacts of reduction in sea ice and then the changes in physical properties on Arctic phytoplankton, numerical models have emerged as valuable tools. In order to generate reliable results, a high quality Arctic Chl-a dataset is essential to improve parameterizations in the coupled ice-ecosystem-ocean circulation models. With the emergency of autonomous platforms (e.g. floats (Argo), autonomous vehicles), high spatial and temporal resolution measurements of biooptical parameters are achievable. However, new challenges arise from the automated way of observing the bio-optical properties of the ocean. Indeed, conversely to what happens when the same kinds of equipment are operated from a ship, these bio-optical data are collected in environmental condiGons that are out of the operator’s control. Therefore, new specific data processing and management procedures have to be developed for in situ bio-optical sensors which generate high spatial and temporal resolution measurements of bio-optical data. In this study, analytical bio-optical techniques are applied to develop quality controlled high quality pan-Arctic long-term information on total biomass of phytoplankton. The quantitative distribution of phytoplankton will be determined on long time scales covering the Fram Strait in the Arctic Ocean by the integration of measurements from various platforms that enable to retrieve the total biomass of phytoplankton