EcoFish WP2 work and Wind, NAO and ecosystemselected

Transcription

1 EcoFish WP2 work and Wind, NAO and ecosystemselected articles Solfrid Sætre Hjøllo Meeting point ECOFISH March 8, 2007

2 Description of EcoFish WP2-work Run, analyze & validate numerical ocean/biology models, and relate the results to exploited stocks. Personell: Einar Svendsen, Morten Skogen, Geir Huse and Solfrid Hjøllo 1. Validation and potential improvements of NORWECOM (a model for physics and phytoplankton). 2. Extention of NORWECOM with IBM modules for zooplankton and fish larvae growth, mortality and distribution 3. Assessment of the need for data assimilation and validation (together with WP3) 4. Perform and analyse historic climate and ecosystem simulations (time-series) for statistical investigations towards fish stocks (e.g. prediction of recruitment (see the ES NOREC application), growth and mortality (early warning)) 5. Define the new format, including resolution in space and time, of multi-species population models being able directly or indirectly to integrate information from the lower trophic levels, fish physiology and behavior 6. Evaluate the contribution of adding ecosystem dynamics 7. Evaluate strategies for meeting the defined requirements for implementing ecosystem dynamics by combining existing models and knowledge on dynamics not already utilized. 8. Perform climate change simulations and investigate the likely consequences on key components

3 NORWECOM The NORWegian ECOlogical Model system A coupled physical-chemical-biological model system applied to study primary production and dispersion of particles (e.g. fish larvae and/or pollution) North Sea, North Atlantic, Benguela, Hardangerfjorden Runs operational (7 days forecast) at met.no Environmental status, eutrophication issues, what-if scenarios, climate/fish relations,.. Main partners: Institute of Marine Research Dept. of Fisheries and Marine Biology, Univ. of Bergen Dept. of Mathematics, Univ of Bergen The Norwegian Meteorological institute

4 OCEAN MODEL Wind Pressure Tides Freshwater (rivers) Atmospheric N SeaSurfaceRadiation Surface heat flux Precipitation Evaporation 3D field of S,T,current, turbulence NORWECOM Primary production Diatoms, flagellates (chatonella) Light model River nutrients Inorganic nitrogen, phosphorus, silicate Respiration Algae death Regeneration Self shading Turbidity Sedimentation Resuspension Denitrification Detritus (N and P) and diatom skeletals (Si) Oxygen Biomass, current Predator field ZOOPLANKTON MODEL Calanus,13 life stages model Lightmodel

5 North Sea primary production Run: North Sea+POM , 10km res Prim.production, nutrients, sedimentation, oxygen, current, hydrography.. Monthly means, daily/2.daily values field+ sections Mean modelled annual NorthSea primary production ( ) (gc/m 2 /year)

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8 Diatoms, monthly prod Color coding: year

9 Flagellates, monthly prod Color coding: year

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13 Climatic impacts

14 Mean annual SST (from HadISST) Climate Impact on Plankton Ecosystems in the Northeast Atlantic A.J.Richardson and D. Schoeman, Science : samples from CPRsurveys => 400 taxa timeseries (phytoplankton, copepod herbivores and zooplankton carnivores) grouped in boxes vs

15 Investigating the form and strength of trophic linkages within the plankton community Note: Copepod herbivores or zoo. carnivores not correlated to SST

16 The magnitude and consistency of the linkages from phytoplakton through copepod herbivores to zooplankton carnivores support dominant bottom-up control within the plankton community Is this an underlying mechanisms for obs. relationships between fish ecology and climate? Incr SST in south =>decr cod recruitment, while visa versa in north Relationships between NAO and fishstocks due to NAO impact on local conditions (SST), propagating up the plankton food web

17 NAO

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20 Cold Polar water Nordic Seas Norway Greenland North Atlantic Orvik & Skagseth (GRL2003) Warm Atlantic water Schematic by B.Hansen NAO+ => SST+, SAT+, SLP+, decline in Arctic Ice Cap& increased Fram Strait export, increased polar heat flux Changed Nordic Sea circulation Biological parameters. Mauritzen, Hjøllo and Sandø, JGR 2006

21 Changing relationship between the North Atlantic Oscillation and key North Atlantic climate parameters Polyakova, E. I., A. G. Journel, I. V. Polyakov, and U. S. Bhatt (2006), Geophys. Res. Lett., 33, L03711, doi: /2005gl Relatingmobile NAO (NAOm) to SST, SAT and SLP Conventional relationships: NAO+ => SST+, SAT+, SLP+, decline in Arctic Ice Cap

22 Figure 1. Standardized time series and running correlations between NAOm and SST, SAT and SLP. (top) The annual (dashed blue line) and the 7-yr running mean (solid blue line) NAOm index time series. For comparison purposes, the smoothed 7-yr running mean AO index time series is also shown in red. (left) The annual (dashed red line) and the 7-yr running mean (solid red line) SST, SAT, and SLP time series. The NAOm index time series is shown as a solid blue line. (right) The running correlations between the NAOm time series and SST, SAT, and SLP time series are shown as the green lines. Upper and lower 95% confidence intervals based on Fisher's method, are shown as red lines; correlations based on the randomization procedure described in Appendix A are shown by blue shading. These running correlations are based on 25-year overlapping windows.

23 Figure 2: Correlation maps between the NAOm index and the (top) SST ( C), (middle) SAT ( C), and (bottom) SLP (hpa) for two time periods with marked different levels of correlations. The differences between the two periods are shown in the right column. Areas with statistical confidence less than 95% are stippled (Fisher's transform is used for these computations).

24 Figure 3. The SST, SAT, SAT-SST, and SLP maps for two periods with different correlation between the NAOm and the other climati parameters. (top) SST ( C) anomalies, (second from top) SAT anomalies ( C), (second from bottom) SST SAT ( C) anomalies, and (bottom) SLP (hpa) and geostrophic wind (m/s) anomalies. The right column shows the difference between the two periods.

25 Relationship NAOm og SAT, SST, SLP alternate between decades of strong and low/negative relationship SST/NAOm positive when SST multidecadal positive, negative otherwise

26 Summary Ocean characteristics important for biology proceeds through food web bottom up Relationships between ocean parameters and NAO are variable => variable NAO biology relationships => need knowledge of processes instead of simple physics-biology relationships

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28 O 2 NIT OXY DIA FLA OXY PHO SIL SiS SIL NIT DETN DETP PHO OXY Stress OXY Stress SIL SiS NIT DETN DETP PHO DETN N 2 DETP BURIED

29 Background and work experince: M.Sc., UiB 1992: Trapped coastal shelf waves PhD., UiB 1999: A comparative study of the Norwegian Sea inflow Met.no: numerical ocean modelling, operational/model development UiB: numerical modelling, teaching Bjerknes centre: analyzing changes in and processes behind Atlantic Inflow, project administration, research coordinator Scientific interests: Nordic Seas hydrography and circulation, numerical modelling, statistical analysis, climate research, popular science At IMR: Working in Group of Oceanography Projects: EcoFish WP2 (+ internal communication), EU-projects RECLAIM & ECOOP (overlapping activities)