2050 2100

1. 2050 2100 Evaluated Hindcast Forecast……… 1960 2000 Presentation to Marine Strategy Coordination Group (MSCG)J Icarus Allen (on behalf of the MEECE consortium)22thFebruary 2012Brussels

2. Scientific Challenge 2050 2100 Evaluated Hindcast Forecast……… 1960 2000 MEECE is a FP7 Integrated Project which aims to push forward the state-of-the-art of our understanding of impacts of global climate change and direct anthropogenic drivers on marine ecosystems end to end.

3. 2050 2100 Evaluated Hindcast Forecast……… 1960 2000 GOALS The specific goals of MEECE are: • To improve the knowledge base on marine ecosystems and their response to climate and anthropogenic driving forces and • To develop innovative predictive management tools and strategies to resolve the dynamic interactions of the global change driver, changes in ocean circulation, climate, ocean acidification, pollution, over fishing and alien invasive species on the structure and functioning of marine ecosystems • To expand the knowledge based and provide scientific tools for the implementation of the European Marine Strategy

4. 2050 2100 Evaluated Hindcast Forecast……… 1960 2000 The Policy Challenge • To improve the knowledge base on marine ecosystems and how they are impacted by anthropogenic and natural driver. • To provide input to governmental and non-governmental actors in the development of innovative tools and strategies for the rebuilding degraded marine ecosystems, protection and the sustainable use of the sea and its resources, in the perspective of the ecosystem approach. • To improve the knowledge base for protection and management scenarios aimed at reconciling the interests of the many economic groups benefiting from the marine resource (including coastal). • To support to EU Marine Strategy (long term ecological objectives), the EU Maritime Policy and the EU Common Fisheries Policy (ecosystem approach to the management of marine resources).

5. The MEECE Approach The MEECE Approach Observations Meta Analysis Parameter-isations Model Systems Experiments Simulations & Synthesis Scenario Definition Indicators Knowledge Transfer Management Strategy Evaluation

6. Experiments and Parameterisations Exposure Experiments Effects of Herbicides on phytoplankton Experiments to inform models Multiple Stressors T, CO2, on phytoplankton, zooplankton, fish larvae T and Pollutants on Phytoplankton, zooplankton, benthic invertebrates Model response parameterised as penalty function on growth. Impacts of Cu and T on Copepod egg production 2050 2100 Evaluated Hindcast Forecast……… 1960 2000

7. Coupled End to End Models 2050 2100 Evaluated Hindcast Forecast……… 1960 2000 Generic Coupler Two Way Coupled Models ERSEM-ECOSIM ROMS-NPZD-OSMOSE ROMS-PICSES-APECOSM NORWECOM.E2E “a thin layer of code for communication and data exchange, enveloped by explicit programming interfaces through which a physical host and any number biogeochemical models can pass information”

8. A Regional Modelling Approach • Modelling allows us • Describe the state of the system and how it may evolve • Represent the dynamics of the pressure - state relationship • Assess the risk on a negative indicator event

9. Model Library

10. Mapping Model Outputs (Characteristics) to Descriptors 2050 2100 Evaluated Hindcast Forecast……… 1960 2000 *ERSEM NW European Shelf Only

11. Fit for Purpose 2050 2100 Evaluated Hindcast Forecast……… 1960 2000 Range of Drivers Range of models Reference Hindcasts

12. Scenarios 2050 2100 Evaluated Hindcast Forecast……… 1960 2000 Past Policy relevant FutureClimate Change Climate forcing 2040-2050 1980-2000 Reanalysis Hindcast Anthropogenic Sensitivity Eutrophication Fishing Pollution Eutrophication Fishing Pollution

13. Changes in State 2050 2100 Evaluated Hindcast Forecast……… 1960 2000 D1 Biodiversity D2 Alien Invasive Species Biogeographic Approach Distribution of potential Habitats for Procentrum Minimum 2100 2000

14. iEutrophication changes Pressure – State Relationships 2050 2100 Evaluated Hindcast Forecast……… 1960 2000 D5 Eutrophication Impact of terrestrial N load changes reduction on Primary Production in the Baltic Sea +50% -50%

15. Probability of a Negative indicator event 2050 2100 Evaluated Hindcast Forecast……… 1960 2000 D5 Eutrophication • How well can we resolve the observed frequency distribution? • How well can we resolve the thresholds? • How might the frequency distribution evolve in the future? • What are the consequences for GES? Winter Nitrate 18% of events above the reference level

16. RiskAssessment Knowledge about Probability Knowledge about outcome High Low Ambiguity (known unknowns) Known outcomes High D1 Biodiversity (habitats) D5 Eutrophication Acidification D8 Pollutants D3 CommercialFishing Pollutants D4 Foodwebs D2 Invasive Species Foodwebs AIS Low Uncertainty (known unknowns) Ignorance (unknown unknowns)

17. Relating an IEA to Models 2050 2100 Evaluated Hindcast Forecast……… 1960 2000 Relating Models to the Integrated Assessment Integrated Ecosystem Assessment –main outputs of models in Relation to importance of ecosystem components as viewed by contributing experts to the IEA

18. Implications for Resource Management 2050 2100 Evaluated Hindcast Forecast……… 1960 2000

19. MEECE Model Atlas 2050 2100 Evaluated Hindcast Forecast……… 1960 2000 Web-based Tool online autumn 2012 Descriptor Fact Sheets: June 2012 Pollutants: Eutrophication: Biodiversity: Invasive species: Commercial species:. Food webs: Hydrography (climate change):

20. Toward Operational Models MEECE Downstream Services MSE Fisheries Eutrophication Pollution? Mulitple driver Context Experiments Scenarios Climate & Anthropogenic Drivers Users Decision Support Tools New Models Data Research models GMES Operational Models (Core Service) Research Operational

21. Lesson Learnt and Future Challenges 2050 2100 Evaluated Hindcast Forecast……… 1960 2000 • MEECE was conceived before the MSFD implementation plan – hence retro fitting to descriptors. • Models have a degree of maturity where a range of outputs are ‘fit for purpose’ • Model development dominated by research push – need user pull • Challenge to improve model skill (requires observations and monitoring) • Challenge , to translate Tbytes of model output into useful information for users • Challenge to demonstrate the usefulness of model outputs to users • Challenge to bring MEECE models to the operational arena • Challenge to develop the next generation of models (experiments, observations etc..)