Presentation to Marine Strategy Coordination Group (MSCG)
J Icarus Allen (on behalf of the MEECE consortium)
22th February 2012Brussels
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Scientific Challenge
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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.
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
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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).
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The MEECE Approach
ObservationsObservations
ExperimentsExperiments
IndicatorsIndicators Knowledge Transfer
Knowledge Transfer
Management Strategy
Evaluation
Management Strategy
Evaluation
Simulations&
Synthesis
Simulations&
Synthesis
ScenarioDefinitionScenario
Definition
Meta AnalysisMeta
AnalysisModel
SystemsModel
SystemsParameter-
isationsParameter-
isations
The MEECE Approach
Experiments and Parameterisations
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Exposure Experiments Effects of Herbicides on phytoplankton
Model response parameterised as penalty function on growth.
Experiments to inform models
Multiple StressorsT, CO2, on phytoplankton, zooplankton, fish larvaeT and Pollutants on Phytoplankton, zooplankton, benthic invertebrates
Impacts of Cu and T on Copepod egg production
Coupled End to End Models
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Generic Coupler Two Way Coupled Models
ERSEM-ECOSIM
ROMS-PICSES-APECOSM
ROMS-NPZD-OSMOSE
“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”
NORWECOM.E2E
A Regional Modelling Approach
BenguelaUpwelling
Biscay AdriaticSea
AtlanticMargin
GLOBAL
NorthSea
BalticSea
BlackSea
BarentsSea
AegeanSea
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
Model Library
Biodiversity Invasive Species
CommercialFisheries
Foodwebs Eutrophi-cation
Seabed integrity
Contaminants
Physio chemical Temperature X (x) (x) Salinity X (x)NutrientNitratePhosphateSilicate
X X X (x)
pH X (x) (x)Biological Features
PhytoplanktonSmallLarge
X (x) x X (x) X
ZooplanktonSmallLarge
X (x) x X (x) X
Fish X (X) x x (x) Chlorophyll X Net Primary Production
(X) X X (X) (X)
Community production
(x) X
Bottom fauna* X (x) X x x X
*ERSEM NW European Shelf Only
Mapping Model Outputs (Characteristics) to Descriptors
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Fit for Purpose
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If we are to use our simulations either for science or policy applications we need to understand and be able to articulate their quality.
TruthT
OResidualf(O-P)
Observation
Prediction
PredictiveError
Observational Error
Validation and verificationRelationships between model and data
Predictiveuncertainty(e.g. numerical error, parameter uncertainty)
Observationalaccuracy, (e.g. measurement error, range of replicates etc.)
Data assimilation isthe art of reducing this distance
P
Model Skill Assessment Quantification of Uncertainty
SourcesScenario uncertaintyStructural/parameter uncertaintyNatural variability (attribution to global change)
Range of Drivers
Range of modelsReference Hindcasts
Scenarios
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Reanalysis Hindcast
Climate forcing
1980-2000 2040-2050
Past Policy relevant Future Climate Change
Anthropogenic Sensitivity
EutrophicationFishingPollution
EutrophicationFishingPollution
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Changes in State
Biogeographic Approach
2000 2100
Distribution of potential Habitats for Procentrum Minimum
D1 BiodiversityD2 Alien Invasive Species
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Pressure – State Relationships
Impact of terrestrial N load changes reduction on Primary Production in the Baltic Sea
D5 Eutrophication
iEutrophication changes
-50% +50%
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Probability of a Negative indicator event 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?
18% of events above the reference level
Winter Nitrate
Knowledge aboutProbability
High
Low
Knowledge about outcome
High Low
Known outcomes Ambiguity (known unknowns)
AIS
Foodwebs
Pollutants
Acidification
D8 Pollutants
D2 Invasive Species
Ignorance (unknown unknowns)
D5 Eutrophication
D3 Commercial
Fishing
Uncertainty (known unknowns)
D4 Foodwebs
D1 Biodiversity (habitats)
Risk Assessment
Relating Models to the Integrated Assessment
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Relating an IEA to Models
Integrated Ecosystem Assessment –main outputs of models inRelation to importance of ecosystem components as viewedby contributing experts to the IEA
Implications for Resource Management
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MEECE Model Atlas
Web-based Tool online autumn 2012
Descriptor Fact Sheets: June 2012
Pollutants: Eutrophication: Biodiversity: Invasive species: Commercial species:.Food webs: Hydrography (climate change):
Toward Operational Models
Research
MSEFisheries
EutrophicationPollution?
Mulitple driver Context
GMESOperational
Models(Core Service)
Decision Support Tools
Experiments
Data
Operational
New Models
Research models
Scenarios
Climate &Anthropogenic
DriversUsers
DownstreamServices
MEECE
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Lesson Learnt and Future Challenges
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..)