1. "Nesting and coupling of physical and biological models “ Albert J. Hermann University of Washington JISAO NOAA/PMEL Collaborators: Dale Haidvogel, Sarah Hinckley, Elizabeth Dobbins, Phyllis Stabeno, Kate Hedstrom, Enrique Curchitser, Dave Musgrave, Georgina Blamey, Bern Megrey

2. OUTLINE General comments General comments Physical BCs Physical BCs Biophysical methods Biophysical methods –pollock in the Gulf of Alaska –salmon in the NE Pacific Conclusions and points of contention Conclusions and points of contention

3. What should coupled, regional, biophysical models include? Mesoscale features and variability Mesoscale features and variability –eddies –coastal trapped waves –fronts Multiple trophic levels Multiple trophic levels Tidal effects Tidal effects –vertical migration interacts with tides -> mean advection of organisms –mixing produces fronts, supplies nutrients

4. The Challenge of Coupled Regional Models Zymurgy's First Law of Evolving System Dynamics “Once you open a can of worms, the only way to re-can them is to use a larger can” Zymurgy's First Law of Evolving System Dynamics “Once you open a can of worms, the only way to re-can them is to use a larger can” Long history of attempted solutions to the BC problem (some involving larger cans) Long history of attempted solutions to the BC problem (some involving larger cans)

5. Robust approaches to the physical BC problem do exist Palma and Matano (2000) survey Palma and Matano (2000) survey –relaxation-radiation hybrid had “best overall performance” Marchesiello et al. (2001) Marchesiello et al. (2001) –nudge weakly for outgoing information –nudge strongly for incoming information –apply sponge near boundary –allow oblique radiation Nudging is suboptimal, but simple and robust Nudging is suboptimal, but simple and robust

6. Tidal and Subtidal Dynamics Need different BCs, but can exist peacefully together in one model Need different BCs, but can exist peacefully together in one model Output data can be tricky; beware of aliasing the tidal signal Output data can be tricky; beware of aliasing the tidal signal Flather (2D) and Marchesiello (3D) solution: Flather (2D) and Marchesiello (3D) solution: –Flather: add/remove water at the free wave speed to match specified SSH at boundary. Nice for 2D tides –Marchesiello: radiate with selective nudging. Nice for everything else (Telescoped solution; worked but had issues) (Telescoped solution; worked but had issues)

7. Biophysical Methods Pollock in the Northern Gulf of Alaska Pollock in the Northern Gulf of Alaska Salmon in the Northeast Pacific Salmon in the Northeast Pacific

8. Coupling Scheme used for Pollock Studies

9. Spatially Explicit IBM: follow individuals in 4 dimensions

10. Known issues of IBMs Particles disperse; need to reseed the population Particles disperse; need to reseed the population Two-way interaction with other species (e.g. the NPZ model) can be tricky Two-way interaction with other species (e.g. the NPZ model) can be tricky –could get unnaturally patchy prey field Single-point Lagrangian statistics can be misleading Single-point Lagrangian statistics can be misleading –spatially variable random walk -> preferentially accumulate particles in areas with low dispersion (proper algorithms avoid this artifact) –However, active particles (e.g. swimming larvae) may in reality accumulate in areas with slower swim speed (not an artifact).

11. So, why not use Eulerian approach for everything? Need a huge number of variables (time of last feeding, what eaten, etc) at every gridpoint to track complicated “history” in Eulerian format Need a huge number of variables (time of last feeding, what eaten, etc) at every gridpoint to track complicated “history” in Eulerian format Eulerian is always looking at the local average individual (which is, on average, dead) and the local average attributes (which are misleading because of nonlinear interactions among species) Eulerian is always looking at the local average individual (which is, on average, dead) and the local average attributes (which are misleading because of nonlinear interactions among species) Harder to encode complicated behaviors in Eulerian format Harder to encode complicated behaviors in Eulerian format In principle, can go back and forth between NPZ and IBM (Eulerian and Lagrangian) techniques (has been done for point dipsersal problems) In principle, can go back and forth between NPZ and IBM (Eulerian and Lagrangian) techniques (has been done for point dipsersal problems)

12. Biophysical Methods Pollock in the Northern Gulf of Alaska Pollock in the Northern Gulf of Alaska Salmon in the Northeast Pacific Salmon in the Northeast Pacific

13. Nested Biophysical Models for GLOBEC: NCEP/MM5 -> ROMS/NPZ -> IBM

14. GLOBEC NPZ model for the CGOA (S. Hinckley) Width of arrow represents N flux

15. Model Nesting Really a form of data assimilation, larger model is “data” Really a form of data assimilation, larger model is “data” Works best if the surface forcing is the same across all scales – o/w easy to get discontinuities (and associated rim currents) across boundaries Works best if the surface forcing is the same across all scales – o/w easy to get discontinuities (and associated rim currents) across boundaries Here, use Marchesiello/Flather BC to feed from coarser to finer grid Here, use Marchesiello/Flather BC to feed from coarser to finer grid Tides (from a tidal model) applied only at smaller scales Tides (from a tidal model) applied only at smaller scales

16. NESTED CIRCULATION MODEL DOMAINS Nested Model Domains

17. Pameters of the Physical Models

18. NPAC Model SSH

19. NESTED MODELS SSH DOY 255 CGOA domain - color NEP domain – b&w AK CA

20. CGOA MODEL SSH DOY 255 Sitka AK Prince William Sound

21. Winds from MM5 (black) and NCEP (white)

22. Flux through Shelikof Strait Black=ADCP data Red=model MM5 NCEP

23. Conclusions/Preferences 1. Avoid spatial boundaries in dynamically active areas 2. Avoid spatial boundaries between different biological communities 3. Consider the different time scales of different variables when setting BCs 4. Two-way coupling is not necessarily better than one-way nesting 5. Visualization matters!

24. 2. Avoid spatial boundaries between different communities (BCs are tricky at the ecotone) Gradient in community structure/limiting nutrient can produce artifacts as reactive materials cross the boundary and seek new “equilibrium” Gradient in community structure/limiting nutrient can produce artifacts as reactive materials cross the boundary and seek new “equilibrium” –Example 1: deep ocean is Fe limited, coastal ocean is not -> spurious bloom at boundary as offshore water moves onshore Impedance change across boundary (different boxes for different system) may lead to discontinuity in biological variables Impedance change across boundary (different boxes for different system) may lead to discontinuity in biological variables

25. Nested Biophysical Models for GLOBEC: NCEP/MM5 -> ROMS/NPZ -> IBM

26. A big bloom occurred at the edge of the coastal NPZ model! Onshore flow Solution: add Fe variable, develop single NPZ model spanning both regions

27. 6. Visualization matters! Easy to miss incorrect features in a 3D field if limited to 2D visuals Easy to miss incorrect features in a 3D field if limited to 2D visuals High-end visualization becomes especially useful when bio variables are added –e.g. where is a nutrient source and who is using it High-end visualization becomes especially useful when bio variables are added –e.g. where is a nutrient source and who is using it Many attributes of biophysical models are truly 3D (e.g. patchy); perturbation signals, mixing Many attributes of biophysical models are truly 3D (e.g. patchy); perturbation signals, mixing Visualize spatial paths from physical model, IBM in 3D Visualize spatial paths from physical model, IBM in 3D

28. 3D view of particles in ROMS Gulf of Alaska simulation (use red/blue glasses for stereo 3D effect) ALASKA Cook Inlet

30. Alaska Salinity Isosurface (32.6 psu)