Summary
The Climate and Marine Production (CAMP) project is advancing understanding of ocean productivity, and its response to climate change, by improving marine ecosystem and biogeochemical models.
The goal is to reduce discrepancies between models outputs when compared with ocean observations to improve past and future projections of ocean productivity. The project will use a new approach based on refining the key parameters that shape model behaviour.
To do this, the team will use high-quality environmental dataset from ESA’s Climate Change Initiative and other global ocean observations to explore how changing conditions influence these parameters. Led by Plymouth Marine Laboratory, Mercator Ocean International and the UK Met Office, CAMP is a collaborative effort between modellers and Earth observation scientists.
Background
The CAMP project addresses the critical challenge of improving the integration of satellite-derived Essential Climate Variables (ECVs) into climate and ecosystem models. While satellite data offer comprehensive, consistent, multi-decadal observations valuable for climate modelling—through data assimilation, model evaluation, and parameter constraint—their uptake remains limited. Barriers include data format issues, mismatched variables between models and remote sensing, and insufficient communication between Earth observation and modelling communities.
To overcome these challenges, the project brings together modellers and CCI ECV providers to enhance collaboration, data usability, and feedback mechanisms. Scientifically, the project focuses on improving marine ecosystem and biogeochemical modelling, particularly marine primary production. It responds to expert-identified priorities including reducing model uncertainty, improving historical reconstructions, and enhancing projections of climate impacts on marine ecosystems—areas vital for supporting IPCC assessments and climate policy.
Aims and objectives
The project aims to address the challenge of improving marine biogeochemical models for climate, with marine primary production serving as the central pivot around which other ecosystem processes and pools are also investigated. We will use three key European biogeochemical models and a satellite-based primary production model. Key to the work is the use of data assimilation and artificial intelligence (AI) tools combined with Climate Change Initiative (CCI) data and field observations to improve models and facilitate intercomparisons. The plan is to reduce model uncertainties, and achieve better convergence across models, thereby increasing confidence in model performance and in climate projections. An added benefit would be new insights into how ecosystem model parameters might be optimised to suit model structure, and to facilitate comparisons across models with differing structures. The synergy between the modelling and the Earth Observation capabilities will be exploited, and the communication barriers between the two communities will be overcome, facilitating future work. Our goal is to influence the strategies and planning of upcoming CMIP activities through the project findings, and through improved communication.
The project objectives are to:
- Use ESA CCI data with biogeochemical models to assess spatiotemporal variability in model parameters.
- Test parameter sensitivity across three ecosystem models (ERSEM, MEDUSA, PISCES) and one primary production model.
- Compare EO and model-based approaches to understand and reduce uncertainty.
- Test models in 1D, regional, and global configurations.
Project plans
CAMP is structured into five work packages (WPs), each addressing a key technical task as follows:
WP1 Science Requirements will advance the requirements and technical specifications outlined in the Statement of Work. It will specify in detail the most suitable earth observation and in situ datasets and data processing methods required to deliver the project. As well as the modelling, assimilation, machine learning and parameter calibration methodology. This work will feed into the subsequent WPs.
WP2 Data Preparation will prepare the WP data as required for the ecosystem models and satellite based primary production model and perform the necessary tailoring of the observation data to be used across WP3.
WP3 Scientific Analysis is broken down into four subtasks:
- WP3.1 Model Parameter Estimation (1D) will implement the 1D model configurations on the global domain,
- WP3.2 Theoretical Comparison and Knowledge Exchange will systematically analyse potential differences between spectral and non-spectral models of photosynthesis to enable comparisons across models,
- WP3.3 User Feedback on ECV Data will collect and collate feedback from WP3.1 and WP3.2. WP3.4 is further broken down into four sub work packages: WP3.4.1 Global Parameter Mapping using Machine Learning will provide model projections into the future, WP3.4.2 Regional Climate Model Projections will use the spatially and temporally variable parametrisation for the ERSEM model to run two climate projections in two regional domains under the SSP3-7.0 scenario, WP3.4.3 Global Climate Model Projections will run until the end of the century, and WP3.4.4 Synthesis, Roadmap, and Manuscripts (s) will synthesis the work undertaken in WP3.
WP4 Outreach and Dissemination will promote the work undertaken in CAMP and work closely with ESA and the Knowledge Exchange team.
WP5 Management will oversee all the WPs and ensure the project delivers to schedule.
Science Co-leader: Prof Shubha Sathyendranath, ssat@pml.ac.uk
Science Co-leader: Dr Stefano Ciavatta, sciavatta@mercator-ocean.fr
Project Manager: Elin Meek, eme@pml.ac.uk
ESA Technical Officer: Sarah Connors, sarah.connors@esa.int

Plymouth Marine Laboratory
Project Prime
Responsible for project management and co-leading science requirements, data preparation, scientific analysis, synthesis and outreach

Mercator Ocean International
Project Partner
Co-leading science leadership and scientific analysis

UK Met Office
Project Partner
Co-leading scientific analysis