NACLIM: North Atlantic Climate Predictability of the Climate in the

1. NACLIM: North Atlantic Climate Predictability 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

2. Core Theme 4 To quantify the impact on oceanic ecosystems and urban societies of predictedNorth Atlantic/Arctic Ocean variability. Marineecosystems Physical environment Urbansocieties

3. Core Theme 4 WP 4.1Impact on the oceanic ecosystem WP 4.2Impact onurban societies

4. NACLIM: North Atlantic Climate Predictability 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

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

6. The Fundamental Question How do we get from here…. Adults …to here.. …and back again? Juveniles

7. Juveniles vs Adults Residuals ~ Environment North-East Atlantic Blue Whiting

8. ”…dismal…”

9. 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 verycomplex • Biologicalscienceslack the quantitative, mechanisticlawscommon in physicalsciences • Correlationvscasuality

10. So what do we do?

11. The approach Workwithinlimitations Lowhangingfruit

12. WP 4.1 Structure Review Detailed Case Studies Generic Approach CMIP5 forecasts SpecificPredictions Assessment of Forecast Skill (WP 1.1, 1.2) ”Lessons learned”

13. 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

14. Blue whiting Zooplankton T 4.1.4 Case Studies Salmon Pilot whales Phytoplankton Puffins

15. e.g. Blue Whiting Spawning Larval observations around Rockall Bank Hátún et al. (2009) CJFAS

16. WP 4.1 Structure Review DetailedCase Studies GenericApproach CMIP5 forecasts SpecificPredictions Assessment of Forecast Skill (WP 1.1, 1.2) ”Lessons learned”

17. T 4.1.2 Generic Approach • ”Match-Mismatch” hypothesis • Larval fish survival depends on match with timing of spring bloom

18. e.g. Scotian Shelf Haddock Platt et al. (2003) Nature

19. T 4.1.2 Generic Approach • 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

20. WP 4.1 Structure Review DetailedCase Studies GenericApproach CMIP5 forecasts SpecificPredictions Assessment of Forecast Skill (WP 1.1, 1.2) ”Lessons learned”

22. T 4.1.3 Making Predictions • Recognise limitations! • Unknown unknowns • Qualitative metrics as well as quantitative • Quality metrics e.g. IPCC style

23. 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?