1. An Overview of Biogeochemical Modeling in ROMS
Kate Hedstrom, ARSC
September 2006
With help from Sarah Hinckley, Katja Fennel, Georgina Gibson, and Hal Batchelder

2. Outline Simple NPZ model Mid-Atlantic Bight model Gulf of Alaska model
GLOBEC Northeast Pacific
ROMS options

3. Annual Phytoplankton Cycle
Strong vertical mixing in winter, low sun angle keep phytoplankton numbers low
Spring sun and reduced winds contribute to stratification, lead to spring bloom
Stratification prevents mixing from bringing up fresh nutrients, plants become nutrient limited, also zooplankton eat down the plants

4. Annual Cycle Continued
In the fall, the grazing animals have declined or gone into winter dormancy, early storms bring in nutrients, get a smaller fall bloom
Winter storms and reduced sun lead to reduced numbers of plants in spite of ample nutrients
We want to model these processes as simply as possible

5. Franks et al. (1986) Model
Simple NPZ (nutrient, phytoplankton, zooplankton) model
Three equations in the three state variables
Closed system - total is conserved

6. The Equations P Change = nutrient uptake - mortality(P) - grazing
Z Change = growth efficiency * grazing - mortality(Z)
N Change = - nutrient uptake + mortality(P) + mortality(Z) + (1 - growth efficiency) * grazing

7. Goal Initial conditions are low P, Z, high N
We want the model to produce a strong spring bloom, followed by reduced numbers for both P and N
Hope to find a steady balance between growth of P and grazing by Z after the bloom

8. Original Franks Model

9. NPZ Results There’s more than one way to damp the oscillations
Change initial conditions
Change grazing function
Change mortality constant
It’s not clear if any of these methods is “right”
Current practice is to add detritus falling out of the mixed layer (NPZD model)

10. Simple NPZD Attempt

11. NPZD Summary
The Franks model is attempting to describe the ocean mixed layer at one point
Next is a 1-D vertical profile of the biology, including light input at the surface
Final goal is a full 3-D model with the physical model providing temperature, currents, seasonal cycle, etc.
Each component is advected and diffused in the same manner as temperature

12. More Modern Models Seven, ten, or more variables
Large and small zooplankton
Large and small phytoplankton
More (specific) nutrients
Detritus
Still missing some fields such as gelatinous zooplankton (salmon food)
Models are tuned for specific regions, specific questions

13. Katja Fennel’s Model NH4 NO3 N2 NH4 NO3 Water column Sediment
Nitrification
Water column
Mineralization
NH4
NO3
Uptake
Phytoplankton
Grazing
Chlorophyll
Zooplankton
Mortality
Large
detritus
Susp.
particles
Nitrification N2
NH4
NO3
Denitrification
Aerobic mineralization
Organic matter
Sediment

14. Fennel’s Model Fasham-type model
In shallow coastal waters, the sinking particles hit the bottom where nutrient remineralization can occur
Add a benthos which contributes a flux of NH4 as a bottom boundary condition
Her goal is to track the nitrogen fluxes and extrapolate to carbon

15. Nested physical-biological model

16. Climatolo-gical heat/fresh-water fluxes
SSH (m)
North Atlantic ROMS
Climatolo-gical heat/fresh-water fluxes
3-day average NCEP winds

18. Middle Atlantic Bight (MAB)
Georges
Bank
Nantucket
Shoals
Hudson
Delaware
Chesapeake
Cape Hatteras
50, 100, 200, m isobaths: dashed lines

19. x 1010 mol N y-1 denitrification removes 90% of all N entering MAB
cross-isobath export of PON, import of DIN
DNF: 5.3 TN
4.2 TN
3.3 DIN
0.9 PON
Rivers: 1.8 TN
2.5 DIN
2.9 PON
0.4 TN
Fennel et al.
(submitted to Global Biogeochemical Cycles)
x 1010 mol N y-1

20. Gulf of Alaska
Sarah Hinckley and Liz Dobbins built a full NPZD model for the shelf and tested it in 1-D
They then ran a 3-D Gulf of Alaska with that model (3 km resolution)
The shelf waters bloomed, as did the offshore waters

22. The Iron Story
They believe that offshore waters don’t bloom due to iron limitation
Iron is brought to the coastal ocean via river sediments
Not enough iron data to do a full model of it
Trace quantities of iron are difficult to measure on steel ships

23. More Iron
People have done experiments at sea, seeding the water with iron - the ocean does bloom after
Adding an iron component to their NPZD model allowed Sarah and Liz to obtain more realistic looking results
It’s not a full iron model, containing nudging to a climatology

24. Iron Climatology -X-axis is cross-shelf distance from GAK1
-Y-axis is depth
-All shelf areas, iron has a value of 2 (not limiting)
-Gradient between coastal and oceanic surface waters, so that oceanic surface waters is depleted
-(Iron input from runoff included)

25. No Iron Limitation – NO3 Alaska Peninsula PWS Kodiak I.
-With no iron limitation term, during late May/ early June (after spring blooms), nitrate is depleted in the surface in all areas (offshore areas should retain high surface nutrients at all times)

26. Iron Limitation – NO3 Alaska Peninsula PWS
With the iron limitation scheme implemented, the oceanic areas no longer show iron depeletion in the surface waters as there is no bloom of phytoplankton due to lowered iron concentrations.

27. Northeast Pacific GLOBEC
Goal is one model that runs off California and Gulf of Alaska
Get the fish prey right, then move on to IBM’s
Track individuals as Lagrangian particles - euphausiids and juvenile coho salmon
Include behavior - swimming vertically
Growth depends on food availability, temperature
Use stored physical and NPZ fields from ROMS

28. ROMS Biology Options NPZD Fasham-type
Two phytoplankton-class model (Lima and Doney)
Small
Diatoms
Silica and carbon
Multiple phytoplankton-class model (ECOSIM)
Four phytoplankton classes
Internal carbon and nitrate for each
No zooplankton

29. Computer Issues
Adding biology doubles or triples the computer time needed for the physical model
These models need more time for more grid points (480x480x30 for CGOA) and also a shorter timestep if the grid spacing is small
They can take weeks of computer time, using many processors in parallel

30. Conclusions Biological modeling is an area of active research
Coupled biogeochemical systems are hot too
Still things to figure out, including the rest of the annual cycle, interannual variability and other changes