GEOHAB modeling activities Wolfgang Fennel Baltic Sea Research Institute Warnemünde (IOW)

1. GEOHAB modeling activities Wolfgang Fennel Baltic Sea Research Institute Warnemünde (IOW) at the University of Rostock

2. GEOHAB Modeling Workshop 2009 workshop is being planned to stimulate the development of modeling in relation to the study of Harmful Algal Blooms (HABs). This workshop is being developed under the auspices of the SCOR/IOC program on Global Ecology and Oceanography of Harmful Algal Blooms.   The Workshop will take place from June 15 to 19, 2009 at the Martin Ryan Institute, National University of Ireland, and is open to graduate students, post-docs and scientists.

3. The overall scientific goal of GEOHAB is to: Improve prediction of HABs by determining the ecological and oceanographic mechanisms underlying their population dynamics, integrating biological, chemical, and physical studies supported by enhanced observation and modelling techniques. SP 2001, IP 2003 http://www.jhu.edu/scor/GEOHABfront.htm

4. Each system has site-specific and universal aspects • Modeling starts with considering of the required • complexity of the model (number of state variables), • resolution of physical processes

5. HABs occur in a variety of systems and settings. ‘Harmfulness’ is a societal, not a scientific, term. (i.e. it does not help to design models) It points to the species that are harmful and defines the locations, where they occur.

6. recommendations of the GMG (Warnemünde meeting, 5-6 April 2002) • “As a mean of identifying needs of regional programs and to interact with the GMG we propose the following list: • Tic list for involvement of modelling in regional GEOHAB programs • What models are required to provide prediction ? • Can the accuracy of the model be ascribed to the required predictions before • they will be useful? (e.g. Is a prediction that there is a 20% chance a HAB • occurring within 1 week, useful?) • What kind of modelling HABs in the region is needed to help understand • processes? • How strong is the occurrence of HABs controlled by physics? • What models of the region do exist? • What needs to be developed and implemented? • How can models help to design field work?”

7. Classes of relevant systems Upwelling systems advanced circulation models coupled to biogeochemical model components (GLOBEC, JGOFS) Eutrophication, marginal seas coupled model systems to run 30-40 years scenarios (climate variations, river discharges), (GLOBEC, JGOFS) Thin layers Modeling thin layers (~ 50cm) in 3D circulation models implies a very high vertical resolution ( challenge) Fjord and embayments local models, site-specific mixing and advection, advanced chemical biological components

8. Modeling can build on existing model system Ocean General Circulation Models, community model, e.g. MOM, ROMS, POM, MICOM MOM  physical/ecosystem modeling in JGOFS, ROMS  physical/ecosystem modeling in GLOBEC regions. 

9. Species of interest and the food-web Can we consider the HAB species alone (low concentration, minor effect on nutrients, weak interaction with grazers)? What is the role of toxicity? Are the cells toxic by pure coincidence or is the toxicity part of the survival strategy? Is toxicity a chemical weapon to keeping away other algae competing for nutrients or to chase away grazers, or making cells just inedible? How can we decide these questions by observations?

10. Live cycle of species of interest Overwintering Dormant stages, resting in sediments? What triggers the awakening of resting stages? Initiation of blooms? Locally, after being released from the sea bed? Accumulation of cells from the open ocean, by on-shore transports? can be decided these by observations, or combining models and observations.

11. Golf of Main (e.g. Anderson et al., 2000; McGillicuddy et al., 2003) Alexandrium fundyense Low abundance, advection of cells important, Weak interaction within the food web, Life cycle: cysts formation important, few state variables, nutrient fields prescribed by climatological data 3d circulation models. Example systems: Baltic Sea (e.g.Kononen 2001; Neumann et al., 2002) Cyanobacteria: Nodularia & Aphanzoimenon Near sea-surface accumulation of biomass Strong interaction within the food web Seasonal succession, Competiton for nutrients, many state variables, 3d physical-chemical-biological-model.

12. How to model HABs • As continuous distributions, [aggregating many individuals in a state variable]? • As individual particles, [individual based models, IBM’s] ?

13. Models range from simple biogeochemical box models or Particle tracking (so-called ’individual based models’) ( first step for development, easy to understand and to use, provide forum for the interdisciplinary dialogue) to 3D coupled physical chemical biological model systems (require super-computers, educated modeller)

14. Most measurements deal practically with • indistinguishable ‘particles’ [within a group], e.g., • cell counts with microscopes, • optical plankton recorders, • acoustic backscatter. • The resulting concentrations represent state variables. • Choice of model depends on the problem at hand.

15. time scales: • A few days: • Predict the spreading of a detected bloom for the next days, •  circulation model with a ‘biological’ tracer • The yearly cycle • Describe the annual cycle of the physical-biological processes, i.e., start, development and ceasing of the bloom in response to forcing scenarios. • full coupled physical and biogeochemical model.

16. HAB‘s and Eutrophication surface accumulations of cyanobacteria Model issues: Coupled 3d circulation and biogeochemical models Forcing data, including river loads

17. The example of the Baltic Sea P-E>0 (60 km3/y) River discharge 480 km3/y Mean depth = 50 m Sill depth = 18 m Vol.= 21 700 km3 Monitoring 5 times a year along the ‘Talweg‘

18. Temperature (winter/summer) Salinity and oxygen

19. 6.7.2001 MODIS NASA (Δx=250m) (courteously, H. Siegel, T. Ohde)

20. bulk-zooplankton can optionally be replaced by a stage resolving model! eggs nauplii copepodites1 copepodites2 adults (Neumann, JMS, 2000;Fennel Neumann, ICES Mar Sci Symp. 2003

21. Surface accumulations are easy to detect, but they are only one aspect! Challenge: to better understand and quantify the seasonal development of Blue-Greens in the water column. Role of stratification is implicitly important, but not the key factor.

22. HAB’s in stratified systems (thin layers)

23. French west coast Gentien, et al. 1998

24. Gentien et al. 1995, DSR 1, Species of interest (Dinophysis, Gymnodinium) were found only in the thin layer !

25. Celtic sea - very thin layer containing a high density accumulation of Karenia mikimotoi (data by courtesy of Robin Raine) transported westwards,upwelled at the southwest corner of Ireland.Raine et al. 2001 Hydrobiologica

26. Building predictive models (t >> a few days)  understanding of biological questions: Why do the cells aggregate in thin layers? Do the cells migrate vertically and what are the controls? e.g., response to light, chemical signals, etc. Proxies for switches to control behavioral patterns as reaction to stimuli. What kind of experiments can be designed to decide these questions?

27. Modeling thin layers (~ 0.5m) in 3D circulation Models implies a very high vertical resolution  problem Way out: high resolution modeling, advection can be very important, e.g., coastal jets, river plumes, up-and downwelling.

28. Example: dynamics of river plumes, stratified plumes

29. Eastern boundariesand Upwelling systems

30. Deep Chlorophyll Maximum, DCM. can be simulated for non-sinking model flagellates, limited by light and nitrate. Model simulation Benguela system, off Angola, 8oS, By courtesy of M.Schmidt Note, the model system is virtually the same as for the Baltic.

31. Alongshore, y-z-section shows that the DCM occurs only in the downwelling, not in the upwelling region. By courtesy of M.Schmidt & S.Schäfer

32. Combining models and observation: Experimental simulations with sinking cells can give a clue about possible deposition areas  to guide field studies (search for seed beds) 60 days, strong wind events initial concentration 0.1g/cm2

33. Summary: Modeling HAB‘s can largely build on existing biogeochemical models (with some modifications). Specific HAB aspects of the models require a quantitative understanding of toxicity for food-web interaction, and of the live cycles.

34. thanks

35. How to model behaviour? If .... then ..... decisions in response to environmental our internal signals (fuzzy logic) Fuzzy logic, a multivalued logic developed to deal with imprecise or vague data. (as opposed to a binary logic, where everything can be expressed in binary terms: 0 or 1, black or white, yes or no) Fuzzy logic allows for a set of values between 0 and 1, shades of gray. Fuzzy logic may used with an expert system, logical inferences can be drawn from imprecise relationships. Fuzzy logic theory was developed by Lofti A. Zadeh at the Univ. of California in the mid 1960s. Or, alternatively, by evaluation functions, which map a set of decisions on a number. Note that if... then ... statements are inefficient in complex model codes.

36. Vertical distribution July 02 April 02 courteously GLOBEC Germany: J. Renz, J. Dutz, C. Möllmann, H.-J. Hirche

37. Evaluation function assesses the quality of the environment, force migration when required Neumann&Fennel, Ocean Modelling, (2005), (in press)

38. Sketch of the fi‘s, which characterize the potential response Neumann& Fennel, Ocean Modelling, (2005), (in press)

39. Neumann& Fennel, Ocean Modelling, (2005), (in press)

40. First Principles? Genetic code versus phenomenological theory? [Starting from genetics seems not (yet) to be feasible] Phenomenological theory postulates that organisms grow, divide and produce toxins. [Knowledge of their genetic constitution is not necessarily required] Properties (nutrient uptake, primary production, grazing,...) and Behaviour (vertical migration, foraging, avoidance, adjustments to habitats,.....) are observable features, which can be quantified and formulated in equations. [The equations do not explain the features, but provide the base to describe system properties, future developments etc.]