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..)