1. S 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 4 Impact on the oceanic ecosystem and urban societies

2. Core Theme 4 To quantify the impact on oceanic ecosystems and urban societies of predicted North Atlantic/Arctic Ocean variability. Physical environment Marine ecosystems Urban societies

3. Core Theme 4 WP 4.1 Impact on the oceanic ecosystem WP 4.2 Impact on urban 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 4.1 Impact 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 Adults Juveniles How do we get from here…. …to here.. …and back again?

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

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 very complex Biological sciences lack the quantitative, mechanistic laws common in physical sciences Correlation vs casuality

10. So what do we do?

11. The approach Low hanging fruitWork within limitations

12. WP 4.1 Structure Review Detailed Case Studies Specific Predictions CMIP5 forecasts Assessment of Forecast Skill (WP 1.1, 1.2) Generic Approach ”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. T 4.1.4 Case Studies Phytoplankton Pilot whales Zooplankton Puffins Blue whiting Salmon

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

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

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

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

19. 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

20. WP 4.1 Structure Review Detailed Case Studies Specific Predictions CMIP5 forecasts Assessment of Forecast Skill (WP 1.1, 1.2) Generic Approach ”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?