The Marine Organic Carbon Atlas Living Lab (MOCA) will advance our ability to consistently and regularly map and assess changes in carbon stocks and fluxes due to biological (organic) processes across a number of EOVs in the pelagic and benthic realms. Focusing on the Arctic Ocean, we will integrate historical and new data from many observing and modelling approaches into a standard format co-designed with relevant stakeholder groups to support regional and global assessments.
Objectives
The Marine Organic Carbon Atlas Living Lab will contribute to the following project objectives:
- help better understand links between biodiversity, biogeochemistry and climate;
- enable development, initialization and validation of ecosystem forecasts and biogeochemical models.
In the MOCA Living Lab we will undertake significant local and regional data rescue efforts related to several EOVs, initiate a new organic carbon data product based on decades-long pelagic and benthic ecosystem monitoring in the Arctic..
The MOCA Living Lab will conduct data rescue efforts to unlock unprecedented amounts of historical data collected in the Arctic using standardised (Darwin Core) metadata and data records. The demo product for the Arctic will be co-created with international modelling communities to ensure direct applicability for biogeochemical and ecosystem models. MOCA will deliver a roadmap towards a novel data product building on the state-of-the-art biological and biogeochemical data, and publish pilot datasets from the Arctic region. Development of the Arctic pilot of MOCA, including its visual formats, will be consulted with specific needs of local (Svalbard, Greenland) authorities and other stakeholders with specific interest in blue carbon and biodiversity monitoring and resource management.
Essential Ocean Variables under study
Main EOVs studied in the Living Lab:
(Click on the EOV name to access the corresponding GOOS EOV Specification Sheet if available)
Phytoplankton biomass and diversity
Zooplankton biomass and diversity
Benthic invertebrate abundance and distribution (EOV Specification Sheet under development)
Seagrass cover and composition
Related project tasks
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Task on “Coordination of the living labs to demonstrate an operational workflow in different settings”
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products and applications.
Task on “Development of new EOV data products and associated roadmaps”
Lead partner: IOPAN
Contributing partners: All partners
This task develops roadmaps and implementation plans towards establishing new biological data products critical for advancing ecosystem, biodiversity and climate projections and global assessments. Roadmaps will be co-created with relevant stakeholders, in particular the modelling communitie and key data integrators. In the process we will test the applicability of the Blueprint workflow specifically for data product development. Substantial data rescue effort targeting publication of long-term biological observations from the Arctic region will be undertaken to deliver a pilot demonstration of the Marine Organic Carbon Atlas. Incorporating remote sensing and model estimates of marine organic carbon stocks and fluxes will help better understand and model the links between biology, biodiversity, biogeochemistry and climate.
Task on “Demonstrated advancement in BioEco forecasting”
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Task on “Establishing early-warning indicators, detecting and assessing impacts of compound events and tipping points”
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Contributing partners: MOi, DTU, CIIMAR, UU, IO PAN
Some of the main challenges addressed in the different focal living labs are identifying and predicting nonlinear changes (such as tipping points), potentially caused by combined effects of different forcing factors. In addition to classical indicators of loss of resilience based on time series, this task considers spatial early warning signals that rely on short-term observations that can be easily obtained and updated. We will recommend and test the performance of a set of potential early-warning indicators. The focus will be on exploring indicators from cable camera observations relevant to detecting changes in macroalgae and seagrass; and new functional plankton diversity indicators derived from a combination of
trait-based models and historical observations.
