S 1 NACLIM: North Atlantic Climate Predictability of the Climate in the North Atlantic/European...

S 1

NACLIM: North Atlantic ClimatePredictability of the Climate in the

North Atlantic/European sector related to North Atlantic/Arctic Ocean temperature

and sea ice variability and change

Core Theme 4Impact on the oceanic ecosystem and urban societies

Core Theme 4

To quantify the impact on oceanic ecosystems and

urban societies of predicted North Atlantic/Arctic Ocean variability.

Physical environment

Marineecosystems

Urbansocieties

Core Theme 4

WP 4.1Impact on the

oceanic ecosystem

WP 4.2Impact on

urban societies

NACLIM: North Atlantic ClimatePredictability of the Climate in the

North Atlantic/European sector related to North Atlantic/Arctic Ocean temperature

and sea ice variability and change

WP 4.1Impact on the oceanic ecosystem

Prediction is difficult, especially if it involves the future.Prediction is difficult, especially if it involves fish.

Niels Bohr

The Fundamental Question

Adults

Juveniles

How do we get from here….

…to here..…and back again?

Juveniles vs Adults

North-East Atlantic Blue Whiting

Residuals ~ Environment

”…dismal…”

So what goes wrong?

•Parental condition

•Sex ratio

•Parental effects

•Atresia

•Disease

•Salinity

•Egg density

•Egg mortality

•Egg predation

•Food amount

•Food availability

•Food type

•Food quality

•Match-mismatch

•Drift

•Temperature

•Competition

•Larval predation

• System is very complex

• Biological sciences lack the quantitative, mechanistic laws common in physical sciences

• Correlation vs casuality

So what do we do?

The approach

Low hanging fruitWork within limitations

WP 4.1 Structure

Review

Detailed Case Studies

SpecificPredictions

CMIP5 forecasts

Assessment of Forecast Skill (WP 1.1, 1.2)

Generic Approach

”Lessons learned”

T 4.1.1/D11 Review

• Review physical-biological coupling• Across all trophic levels – plankton to whales• Not just productivity (recruitment)

• Classify according to level of understanding• Mechanistic or correlative? Robustness?• Based on specific features or large scale indices?

• Identify the low-hanging fruit• i.e. the strongest physical-biological couplings

T 4.1.4 Case Studies

Phytoplankton

Pilotwhales

Zooplankton

Puffins

Blue whitingSalmon

e.g. Blue Whiting Spawning

Larval observations around Rockall Bank

Hátún et al. (2009) CJFAS

WP 4.1 Structure

Review

DetailedCase Studies

SpecificPredictions

CMIP5 forecasts

Assessment of Forecast Skill (WP 1.1, 1.2)

GenericApproach

”Lessons learned”

• ”Match-Mismatch” hypothesis

• Larval fish survival depends on match with timing of spring bloom

T 4.1.2 Generic Approach

e.g. Scotian Shelf Haddock

Platt et al. (2003) Nature

• Assess ability of CMIP5 models to capture spring bloom timing• Where possible!• Develop time series of timings

• Identify fish populations that show sensitivity to bloom timing• Meta-analytic approach

• Predict where possible

T 4.1.2 Generic Approach

WP 4.1 Structure

Review

DetailedCase Studies

SpecificPredictions

CMIP5 forecasts

Assessment of Forecast Skill (WP 1.1, 1.2)

GenericApproach

”Lessons learned”

T 4.1.3 Making Predictions

• Recognise limitations! • Unknown unknowns

• Qualitative metrics as well as quantitative

• Quality metrics e.g. IPCC style

D52 ”Lessons Learned”

• Review paper

• Where are the knowledge gaps?

• What needs to be done in the future?

• What are the strengths and weaknesses of our approach?