S 1 NACLIM: North Atlantic Climate Predictability of the Climate in the North Atlantic/European...
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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
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Core Theme 4
To quantify the impact on oceanic ecosystems and
urban societies of predicted North Atlantic/Arctic Ocean variability.
Physical environment
Marineecosystems
Urbansocieties
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Core Theme 4
WP 4.1Impact on the
oceanic ecosystem
WP 4.2Impact on
urban societies
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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
![Page 5: S 1 NACLIM: North Atlantic Climate Predictability of the Climate in the North Atlantic/European sector related to North Atlantic/Arctic Ocean temperature.](https://reader035.fdocuments.us/reader035/viewer/2022062713/56649f4a5503460f94c6c751/html5/thumbnails/5.jpg)
Prediction is difficult, especially if it involves the future.Prediction is difficult, especially if it involves fish.
Niels Bohr
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The Fundamental Question
Adults
Juveniles
How do we get from here….
…to here..…and back again?
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Juveniles vs Adults
North-East Atlantic Blue Whiting
Residuals ~ Environment
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”…dismal…”
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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
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So what do we do?
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The approach
Low hanging fruitWork within limitations
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WP 4.1 Structure
Review
Detailed Case Studies
SpecificPredictions
CMIP5 forecasts
Assessment of Forecast Skill (WP 1.1, 1.2)
Generic Approach
”Lessons learned”
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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
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T 4.1.4 Case Studies
Phytoplankton
Pilotwhales
Zooplankton
Puffins
Blue whitingSalmon
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e.g. Blue Whiting Spawning
Larval observations around Rockall Bank
Hátún et al. (2009) CJFAS
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WP 4.1 Structure
Review
DetailedCase Studies
SpecificPredictions
CMIP5 forecasts
Assessment of Forecast Skill (WP 1.1, 1.2)
GenericApproach
”Lessons learned”
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• ”Match-Mismatch” hypothesis
• Larval fish survival depends on match with timing of spring bloom
T 4.1.2 Generic Approach
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e.g. Scotian Shelf Haddock
Platt et al. (2003) Nature
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• 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
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WP 4.1 Structure
Review
DetailedCase Studies
SpecificPredictions
CMIP5 forecasts
Assessment of Forecast Skill (WP 1.1, 1.2)
GenericApproach
”Lessons learned”
![Page 21: S 1 NACLIM: North Atlantic Climate Predictability of the Climate in the North Atlantic/European sector related to North Atlantic/Arctic Ocean temperature.](https://reader035.fdocuments.us/reader035/viewer/2022062713/56649f4a5503460f94c6c751/html5/thumbnails/21.jpg)
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T 4.1.3 Making Predictions
• Recognise limitations! • Unknown unknowns
• Qualitative metrics as well as quantitative
• Quality metrics e.g. IPCC style
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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?
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